Read Indeed job postings and salary data with one API call

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        "description_html": "<div>Cox Automotive is deploying enterprise AI capabilities on AWS Quick across the enterprise, helping teams work more effectively in their day-to-day operations.<br>\n<br>\nThe Software Engineer II applies secure software engineering principles to the design, development, testing, maintenance, and evaluation of software and cloud infrastructure. This role collaborates closely with teammates to understand business requirements, contribute to architectural discussions, and deliver scalable, resilient, and secure systems. As part of a corporate-wide AI transformation initiative, this role will also incorporate the responsible use of AI-assisted development tools to streamline coding, testing, troubleshooting, and documentation activities across the SDLC.<br>\n<br>\nThis is early-stage work with executive sponsorship, direct access to AWS technical teams, and the chance to grow your skills rapidly in AI platform engineering.<br>\n<br>\n<b>What You'll Do</b><br>\n<br>\n<b>Build &amp; Ship<br>\n</b><br>\n<br>\n<ul><li><b>Build and maintain connectors</b> that integrate Quick with enterprise systems - wrapping APIs, handling auth, managing errors, and making data available to AI agents.</li><li><b>\nShip features on the AI Artifact Hub</b> - bug fixes, performance improvements, and new capabilities that make the product better for internal users.</li><li><b>\nWrite clean, tested Python code</b> for APIs, data pipelines, agent skills, and platform tooling.</li><li><b>\nHelp enforce integration standards</b> and support domain teams as they publish MCP servers to the enterprise connector catalog.</li><li><b>\nImplement features end-to-end:</b> design, code, test, deploy, and monitor in production.</li></ul><br>\n<b>\nLearn &amp; Grow<br>\n</b><br>\n<br>\n<ul><li><b>Work closely with senior engineers</b> to learn architecture patterns, code review practices, and production engineering standards.</li><li><b>\nDevelop expertise in AI platform concepts:</b> RAG, knowledge ingestion, embeddings, agent orchestration, evaluation frameworks, and access control for AI systems.</li><li><b>\nGrow your AWS skills</b> through hands-on work with Lambda, S3, IAM, API Gateway, Step Functions, and infrastructure as code.</li><li><b>\nContribute to access control and data safety</b> - help ensure AI agents respect user permissions and never surface unauthorized data in prompt contexts.</li><li><b>\nParticipate in design discussions</b> - your perspective matters on a small team.</li></ul><br>\n<b>\nOperate &amp; Support<br>\n</b><br>\n<br>\n<ul><li><b>Instrument the systems you build</b> - metrics, logging, alerting, and dashboards so the team knows when something is wrong before users do.</li><li><b>\nContribute to evaluation and feedback systems</b> - help build automated test suites that measure agent quality and capture human-in-the-loop corrections.</li><li><b>\nWrite and maintain CI/CD pipelines (GitHub Actions)</b> and infrastructure as code (Terraform) with guidance from senior team members.</li><li><b>\nContribute to documentation</b> for systems, runbooks, and onboarding materials.</li><li><b>\nParticipate in on-call rotation</b> with mentorship and support from senior engineers.</li></ul><br>\n<b>\nWho You Are</b><br>\n<ul><li>\nBachelor's degree in Computer Science and 2 years' experience in a related field. The right candidate could also have a different combination, such as a<br>\n<br>\nmaster's degree and up to 2 years' experience; or 14 years' experience in a related field.</li><li>\n2+ years of professional software development experience building production applications or services.</li><li>\nProficiency in Python, including writing APIs, scripts, or data processing logic.</li><li>\nFamiliarity with AWS cloud services (e.g., Lambda, S3, IAM, API Gateway) or another major cloud provider.</li><li>\nExperience with version control (Git) and collaborative development workflows (pull requests, code review).</li><li>\nBasic understanding of APIs (REST, authentication patterns) and experience integrating with third-party services.</li><li>\nEagerness to learn new technologies and grow into more complex engineering challenges.</li><li>\nStrong communication skills and a collaborative, team-first mindset.</li><li>\nApplicants must be authorized to work in the United States for any employer without current or future sponsorship.</li><li>\nAbility to work in the office three days per week.</li><li>\nWillingness to participate in an on-call rotation with mentorship from senior engineers.</li></ul><br>\n<b>\nPreferred Qualifications</b><br>\n<ul><li>\nExposure to AI/ML-powered tools, AI APIs, prompt engineering, or building AI-enabled features.</li><li>\nExperience with Infrastructure as Code (Terraform) or CI/CD pipelines (GitHub Actions).</li><li>\nFamiliarity with the Model Context Protocol (MCP) or building tool/plugin interfaces for AI systems.</li><li>\nFamiliarity with data platforms (Snowflake, Databricks, data lakes, or similar).</li><li>\nExposure to authorization models in multi-user systems (row-level security, role-based access).</li><li>\nFamiliarity with cost optimization or FinOps concepts in cloud environments.</li><li>\nInterest in or exposure to knowledge graphs, embeddings, vector databases, or search systems.</li><li>\nExperience with event-driven architectures or serverless computing patterns.</li><li>\nExperience building internal tools, developer platforms, or internal-facing products.</li><li>\nPrior work in automotive, media, or another large enterprise environment is a plus but not required.</li></ul><br>\nUSD 89,400.00 - 134,000.00 per year<br>\n<br>\n<b>Compensation:</b><br>\n<br>\nCompensation includes a base salary in the range of $89,400.00 - $134,000.00. The base salary may vary within the anticipated base pay range based on factors such as the ultimate location of the position and the selected candidate's knowledge, skills, and abilities. Position may be eligible for additional compensation that may include an incentive program.<br>\n<br>\n<b>Benefits:</b><br>\n<br>\nThe Company offers eligible employees the flexibility to take as much vacation with pay as they deem consistent with their duties, the company's needs, and its obligations; seven paid holidays throughout the calendar year; and up to 160 hours of paid wellness annually for their own wellness or that of family members. Employees are also eligible for additional paid time off in the form of bereavement leave, time off to vote, jury duty leave, volunteer time off, military leave, and parental leave.<br>\n<br>\nEOE, including disability/vets</div>",
        "description_text": "Cox Automotive is deploying enterprise AI capabilities on AWS Quick across the enterprise, helping teams work more effectively in their day-to-day operations. The Software Engineer II applies secure software engineering principles to the design, development, testing, maintenance, and evaluation of software and cloud infrastructure. This role collaborates closely with teammates to understand business requirements, contribute to architectural discussions, and deliver scalable, resilient, and secure systems. As part of a corporate-wide AI transformation initiative, this role will also incorporate the responsible use of AI-assisted development tools to streamline coding, testing, troubleshooting, and documentation activities across the SDLC. This is early-stage work with executive sponsorship, direct access to AWS technical teams, and the chance to grow your skills rapidly in AI platform engineering. What You'll Do Build & Ship Build and maintain connectors that integrate Quick with enterprise systems - wrapping APIs, handling auth, managing errors, and making data available to AI agents. Ship features on the AI Artifact Hub - bug fixes, performance improvements, and new capabilities that make the product better for internal users. Write clean, tested Python code for APIs, data pipelines, agent skills, and platform tooling. Help enforce integration standards and support domain teams as they publish MCP servers to the enterprise connector catalog. Implement features end-to-end: design, code, test, deploy, and monitor in production. Learn & Grow Work closely with senior engineers to learn architecture patterns, code review practices, and production engineering standards. Develop expertise in AI platform concepts: RAG, knowledge ingestion, embeddings, agent orchestration, evaluation frameworks, and access control for AI systems. Grow your AWS skills through hands-on work with Lambda, S3, IAM, API Gateway, Step Functions, and infrastructure as code. Contribute to access control and data safety - help ensure AI agents respect user permissions and never surface unauthorized data in prompt contexts. Participate in design discussions - your perspective matters on a small team. Operate & Support Instrument the systems you build - metrics, logging, alerting, and dashboards so the team knows when something is wrong before users do. Contribute to evaluation and feedback systems - help build automated test suites that measure agent quality and capture human-in-the-loop corrections. Write and maintain CI/CD pipelines (GitHub Actions) and infrastructure as code (Terraform) with guidance from senior team members. Contribute to documentation for systems, runbooks, and onboarding materials. Participate in on-call rotation with mentorship and support from senior engineers. Who You Are Bachelor's degree in Computer Science and 2 years' experience in a related field. The right candidate could also have a different combination, such as a master's degree and up to 2 years' experience; or 14 years' experience in a related field. 2+ years of professional software development experience building production applications or services. Proficiency in Python, including writing APIs, scripts, or data processing logic. Familiarity with AWS cloud services (e.g., Lambda, S3, IAM, API Gateway) or another major cloud provider. Experience with version control (Git) and collaborative development workflows (pull requests, code review). Basic understanding of APIs (REST, authentication patterns) and experience integrating with third-party services. Eagerness to learn new technologies and grow into more complex engineering challenges. Strong communication skills and a collaborative, team-first mindset. Applicants must be authorized to work in the United States for any employer without current or future sponsorship. Ability to work in the office three days per week. Willingness to participate in an on-call rotation with mentorship from senior engineers. Preferred Qualifications Exposure to AI/ML-powered tools, AI APIs, prompt engineering, or building AI-enabled features. Experience with Infrastructure as Code (Terraform) or CI/CD pipelines (GitHub Actions). Familiarity with the Model Context Protocol (MCP) or building tool/plugin interfaces for AI systems. Familiarity with data platforms (Snowflake, Databricks, data lakes, or similar). Exposure to authorization models in multi-user systems (row-level security, role-based access). Familiarity with cost optimization or FinOps concepts in cloud environments. Interest in or exposure to knowledge graphs, embeddings, vector databases, or search systems. Experience with event-driven architectures or serverless computing patterns. Experience building internal tools, developer platforms, or internal-facing products. Prior work in automotive, media, or another large enterprise environment is a plus but not required. USD 89,400.00 - 134,000.00 per year Compensation: Compensation includes a base salary in the range of $89,400.00 - $134,000.00. The base salary may vary within the anticipated base pay range based on factors such as the ultimate location of the position and the selected candidate's knowledge, skills, and abilities. Position may be eligible for additional compensation that may include an incentive program. Benefits: The Company offers eligible employees the flexibility to take as much vacation with pay as they deem consistent with their duties, the company's needs, and its obligations; seven paid holidays throughout the calendar year; and up to 160 hours of paid wellness annually for their own wellness or that of family members. 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        "description_html": "<div>Cox Automotive is deploying enterprise AI capabilities on AWS Quick across the enterprise, helping teams work more effectively in their day-to-day operations.<br>\n<br>\nWe are looking for a Senior Software Engineer who applies the principles of secure software engineering to the design, development, maintenance, testing, and evaluation of software and cloud infrastructure that supports this initiative.<br>\n<br>\nAs a senior member of the engineering organization, you will be expected to contribute beyond feature implementation by influencing development practices, mentoring teammates, raising engineering standards, and helping guide technical decision-making across products and services. You'll regularly balance short-term delivery objectives with long-term maintainability, scalability, and operational excellence.<br>\n<br>\nThis is early-stage work with executive sponsorship, direct access to AWS technical teams, and the autonomy to shape how the platform is built.<br>\n<br>\n<b>What You'll Do</b><br>\n<br>\n<b>Platform Integration &amp; Connector Governance<br>\n</b><br>\n<br>\n<ul><li><b>Own the enterprise connector catalog</b> - define integration standards, security review gates, and onboarding playbooks for teams publishing MCP servers to Quick.</li><li><b>\nBuild platform-level integration infrastructure:</b> auth plumbing (Entra/OAuth 2.0), connector health monitoring, and lifecycle tooling.</li><li><b>\nServe as the senior technical contact for domain teams</b> (Snowflake, Salesforce, ServiceNow, Seismic) building MCP servers - unblock them, enforce guardrails, review designs, and ensure production readiness.</li><li><b>\nBuild connectors for systems where no dedicated team exists</b> - AS400 wholesale auction databases, multi-step Databricks pipelines, internal REST/gRPC services, or whatever the business needs next.</li></ul><br>\n<b>\nAI Platform Engineering &amp; Product Development<br>\n</b><br>\n<br>\n<ul><li><b>Own and evolve the AI Artifact Hub</b> - harden the product, improve reliability, and ship features that make it a core part of how the org builds with AI.</li><li><b>\nBuild and improve knowledge ingestion pipelines</b> - chunking strategies, metadata extraction, and retrieval quality tuning.</li><li><b>\nDevelop agents, skills, and automation workflows</b> that solve real business problems through the Quick platform.</li><li><b>\nDesign and implement fine-grained access controls and DLP enforcement</b> - when an agent queries Snowflake or Office 365 on behalf of a user, it must strictly respect row-level permissions and never surface unauthorized data in prompt contexts.</li><li><b>\nAdopt spec-driven, verification-first development practices</b> - define expected behavior before implementation, especially when building with AI-assisted tooling.</li></ul><br>\n<b>\nReliability, Observability &amp; Evaluation<br>\n</b><br>\n<br>\n<ul><li><b>Build the operational backbone for AI at scale</b> - cost tracking by team/department, proactive circuit breakers for runaway token loops, and fallback handling when underlying services degrade.</li><li><b>\nDesign evaluation frameworks and automated &quot;ground truth&quot; test suites</b> - measure whether changes to agent instructions improve or degrade accuracy across dozens of business workflows before they hit production.</li><li><b>\nBuild feedback loop APIs</b> (Lambda, Step Functions) that capture human-in-the-loop corrections and feed quality signals back into the platform.</li><li><b>\nImplement observability across the stack:</b> usage metrics, latency, error rates, cost dashboards, and security-aware alerting.</li></ul><br>\n<b>\nCollaboration &amp; Growth<br>\n</b><br>\n<br>\n<ul><li><b>Participate in design and code reviews.</b> Mentor junior engineers through pairing and knowledge sharing.</li><li><b>\nCollaborate with the Principal Engineer</b> on architecture decisions and technical direction.</li><li><b>\nWork directly with AWS technical teams</b> to troubleshoot issues, provide feedback, and adopt new capabilities.</li></ul><br>\n<b>\nWho You Are</b><br>\n<ul><li>\nBachelor's degree (list any requirements of discipline here) and 4 years' experience in a related field. The right candidate could also have a different combination, such as a master's degree and 2 years' experience; a Ph.D. and up to 1 year of experience; or 16years' experience in a related field.</li><li>\n5+ years of professional software development experience, including designing, building, and operating production systems.</li><li>\nStrong proficiency in Python, including building APIs, data pipelines, and integrations.</li><li>\nHands-on experience with AWS services (Lambda, S3, IAM, API Gateway, CloudWatch, Step Functions, or similar).</li><li>\nExperience building integrations with third-party APIs and SaaS platforms, including authentication flows (OAuth 2.0, OIDC).</li><li>\nExperience with Infrastructure as Code (Terraform preferred) and CI/CD pipelines (GitHub Actions preferred).</li><li>\nSolid understanding of distributed systems concepts: fault tolerance, idempotency, eventual consistency, and graceful degradation.</li><li>\nExperience designing and enforcing authorization policies in multi-tenant or multi-user systems (row-level security, ABAC, or similar).</li><li>\nComfort working in a small, fast-moving team where you own what you build end-to-end.</li><li>\nStrong communication skills. Able to explain technical decisions clearly and collaborate across teams.</li><li>\nApplicants must be authorized to work in the United States for any employer without current or future sponsorship.</li><li>\nAbility to work in the office three days per week.</li><li>\nWillingness to participate in an on-call rotation for production platform systems.</li></ul><br>\n<b>\nPreferred Qualifications</b><br>\n<ul><li>\nExperience with AI/ML-powered systems, with exposure to emerging patterns such as RAG, knowledge retrieval, or agent-based architectures.</li><li>\nExperience with Amazon Bedrock, AWS managed AI/ML services, or a comparable enterprise AI platform.</li><li>\nFamiliarity with the Model Context Protocol (MCP) or experience building agent/tool integration layers.</li><li>\nExperience with Snowflake, semantic views, or similar data platform technologies.</li><li>\nBackground in knowledge graphs, embeddings, vector stores, or enterprise search/retrieval systems.</li><li>\nExperience building evaluation or testing frameworks for non-deterministic systems (ML model eval, A/B testing infrastructure, or similar).</li><li>\nFamiliarity with cost optimization in cloud-native architectures - FinOps thinking, usage metering, or chargeback systems.</li><li>\nExperience with data loss prevention, PII detection/redaction, or security controls in data pipelines.</li><li>\nExperience with event-driven architectures or workflow engines (Step Functions, Temporal).</li><li>\nExperience building or operating internal developer platforms or developer tooling.</li><li>\nExperience taking ownership of an inherited codebase and improving it to a well-documented, supportable state.</li><li>\nPrior work in automotive, media, or another large enterprise with a complex system landscape is an advantage but not required.</li></ul><br>\nUSD 101,500.00 - 169,100.00 per year<br>\n<br>\n<b>Compensation:</b><br>\n<br>\nCompensation includes a base salary in the range of $101,500.00 - $169,100.00. The base salary may vary within the anticipated base pay range based on factors such as the ultimate location of the position and the selected candidate's knowledge, skills, and abilities. Position may be eligible for additional compensation that may include an incentive program.<br>\n<br>\n<b>Benefits:</b><br>\n<br>\nThe Company offers eligible employees the flexibility to take as much vacation with pay as they deem consistent with their duties, the company's needs, and its obligations; seven paid holidays throughout the calendar year; and up to 160 hours of paid wellness annually for their own wellness or that of family members. Employees are also eligible for additional paid time off in the form of bereavement leave, time off to vote, jury duty leave, volunteer time off, military leave, and parental leave.<br>\n<br>\nEOE, including disability/vets</div>",
        "description_text": "Cox Automotive is deploying enterprise AI capabilities on AWS Quick across the enterprise, helping teams work more effectively in their day-to-day operations. We are looking for a Senior Software Engineer who applies the principles of secure software engineering to the design, development, maintenance, testing, and evaluation of software and cloud infrastructure that supports this initiative. As a senior member of the engineering organization, you will be expected to contribute beyond feature implementation by influencing development practices, mentoring teammates, raising engineering standards, and helping guide technical decision-making across products and services. You'll regularly balance short-term delivery objectives with long-term maintainability, scalability, and operational excellence. This is early-stage work with executive sponsorship, direct access to AWS technical teams, and the autonomy to shape how the platform is built. What You'll Do Platform Integration & Connector Governance Own the enterprise connector catalog - define integration standards, security review gates, and onboarding playbooks for teams publishing MCP servers to Quick. Build platform-level integration infrastructure: auth plumbing (Entra/OAuth 2.0), connector health monitoring, and lifecycle tooling. Serve as the senior technical contact for domain teams (Snowflake, Salesforce, ServiceNow, Seismic) building MCP servers - unblock them, enforce guardrails, review designs, and ensure production readiness. Build connectors for systems where no dedicated team exists - AS400 wholesale auction databases, multi-step Databricks pipelines, internal REST/gRPC services, or whatever the business needs next. AI Platform Engineering & Product Development Own and evolve the AI Artifact Hub - harden the product, improve reliability, and ship features that make it a core part of how the org builds with AI. Build and improve knowledge ingestion pipelines - chunking strategies, metadata extraction, and retrieval quality tuning. Develop agents, skills, and automation workflows that solve real business problems through the Quick platform. Design and implement fine-grained access controls and DLP enforcement - when an agent queries Snowflake or Office 365 on behalf of a user, it must strictly respect row-level permissions and never surface unauthorized data in prompt contexts. Adopt spec-driven, verification-first development practices - define expected behavior before implementation, especially when building with AI-assisted tooling. Reliability, Observability & Evaluation Build the operational backbone for AI at scale - cost tracking by team/department, proactive circuit breakers for runaway token loops, and fallback handling when underlying services degrade. Design evaluation frameworks and automated \"ground truth\" test suites - measure whether changes to agent instructions improve or degrade accuracy across dozens of business workflows before they hit production. Build feedback loop APIs (Lambda, Step Functions) that capture human-in-the-loop corrections and feed quality signals back into the platform. Implement observability across the stack: usage metrics, latency, error rates, cost dashboards, and security-aware alerting. Collaboration & Growth Participate in design and code reviews. Mentor junior engineers through pairing and knowledge sharing. Collaborate with the Principal Engineer on architecture decisions and technical direction. Work directly with AWS technical teams to troubleshoot issues, provide feedback, and adopt new capabilities. Who You Are Bachelor's degree (list any requirements of discipline here) and 4 years' experience in a related field. The right candidate could also have a different combination, such as a master's degree and 2 years' experience; a Ph.D. and up to 1 year of experience; or 16years' experience in a related field. 5+ years of professional software development experience, including designing, building, and operating production systems. Strong proficiency in Python, including building APIs, data pipelines, and integrations. Hands-on experience with AWS services (Lambda, S3, IAM, API Gateway, CloudWatch, Step Functions, or similar). Experience building integrations with third-party APIs and SaaS platforms, including authentication flows (OAuth 2.0, OIDC). Experience with Infrastructure as Code (Terraform preferred) and CI/CD pipelines (GitHub Actions preferred). Solid understanding of distributed systems concepts: fault tolerance, idempotency, eventual consistency, and graceful degradation. Experience designing and enforcing authorization policies in multi-tenant or multi-user systems (row-level security, ABAC, or similar). Comfort working in a small, fast-moving team where you own what you build end-to-end. Strong communication skills. Able to explain technical decisions clearly and collaborate across teams. Applicants must be authorized to work in the United States for any employer without current or future sponsorship. Ability to work in the office three days per week. Willingness to participate in an on-call rotation for production platform systems. Preferred Qualifications Experience with AI/ML-powered systems, with exposure to emerging patterns such as RAG, knowledge retrieval, or agent-based architectures. Experience with Amazon Bedrock, AWS managed AI/ML services, or a comparable enterprise AI platform. Familiarity with the Model Context Protocol (MCP) or experience building agent/tool integration layers. Experience with Snowflake, semantic views, or similar data platform technologies. Background in knowledge graphs, embeddings, vector stores, or enterprise search/retrieval systems. Experience building evaluation or testing frameworks for non-deterministic systems (ML model eval, A/B testing infrastructure, or similar). Familiarity with cost optimization in cloud-native architectures - FinOps thinking, usage metering, or chargeback systems. Experience with data loss prevention, PII detection/redaction, or security controls in data pipelines. Experience with event-driven architectures or workflow engines (Step Functions, Temporal). Experience building or operating internal developer platforms or developer tooling. Experience taking ownership of an inherited codebase and improving it to a well-documented, supportable state. Prior work in automotive, media, or another large enterprise with a complex system landscape is an advantage but not required. 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        "description_html": "<div>Cox Automotive is deploying enterprise AI capabilities on AWS Quick across the enterprise, helping teams work more effectively in their day-to-day operations.<br>\n<br>\nWe are seeking a Principal Software Engineer to own technical direction for the platform, the applications, and integrations around it. This role shapes architecture across two products: AWS Quick, an emerging AI platform for agents and enterprise connectors; and an established internal AI Artifact Hub used across Cox Automotive that lets engineering and product teams publish and share interactive artifacts through a web experience and an MCP server interface.<br>\n<br>\nThis is a hands-on technical leadership role. You will make the key architecture and engineering decisions, lead engineers through implementation, and stay close enough to the code to review designs, debug difficult issues, and contribute where it matters. The team builds connectors, agents, knowledge bases and data pipelines, and quality systems that integrate Quick with Cox Automotive's core enterprise tools and operational systems.<br>\n<br>\nThis is early-stage work with executive sponsorship, direct access to AWS technical teams, and the autonomy to define the architecture.<br>\n<br>\n<b>What You'll Do</b><br>\n<br>\n<b>Architecture &amp; Technical Direction<br>\n</b><br>\n<br>\n<ul><li><b>Own the end-to-end architecture</b> for the AWS Quick customization layer, including connectors, agent orchestration, knowledge ingestion, service boundaries, and infrastructure as code.</li><li><b>\nDefine the Model Context Protocol (MCP) strategy</b> for enterprise integrations: tool contracts, governance, authentication, authorization, and safety boundaries.</li><li><b>\nOwn the technical direction of the AI Artifact Hub.</b> Assess its current state, define a hardening plan, and improve reliability as adoption grows.</li><li><b>\nMake and document major architecture decisions,</b> including the trade-offs behind them. Build for near-term delivery without closing off the platform's next stage.</li><li><b>\nWork with Enterprise Architecture, Security, Cloud Automation, and AWS technical teams</b> to align platform direction and resolve cross-team issues.</li></ul><br>\n<b>\nAI Platform Engineering &amp; Quality<br>\n</b><br>\n<br>\n<ul><li><b>Own the quality and evaluation approach</b> for AI capabilities, including automated regression tests, measurable acceptance criteria, and human-in-the-loop (HITL) feedback.</li><li><b>\nLead technical decisions around enterprise knowledge and retrieval,</b> including ingestion, content curation, metadata, knowledge graphs, retrieval quality, context management, and platform-supported RAG configuration.</li><li><b>\nDefine standards and reusable patterns</b> for agents and skills.</li><li><b>\nSet the design for MCP servers, tool interfaces, and structured APIs</b> intended for agent use.</li><li><b>\nEstablish spec-driven, verification-first practices</b> for AI-assisted software development. Evaluate new capabilities and adopt them when they improve delivery without weakening quality or security.</li></ul><br>\n<b>\nResilient Distributed Systems<br>\n</b><br>\n<br>\n<ul><li><b>Set production reliability standards</b> for AI-enabled integrations, including monitoring, observability, graceful degradation, and incident response.</li><li><b>\nDefine failure and recovery patterns:</b> timeouts, bounded retries with backoff, circuit breakers, and clear fallback behavior.</li><li><b>\nDefine idempotency and durable workflow patterns</b> for operations that cross service or third-party boundaries.</li><li><b>\nLead response to significant platform incidents</b> and drive the follow-up engineering work.</li></ul><br>\n<b>\nTechnical Leadership &amp; Influence<br>\n</b><br>\n<br>\n<ul><li><b>Lead architecture and design reviews.</b> Pair with engineers on the hardest problems and raise the team's engineering judgment.</li><li><b>\nGuide the team's Python implementation</b> through design, code review, debugging, and targeted production contributions.</li><li><b>\nServe as a senior technical contact for AWS</b> on platform architecture, product capabilities, and roadmap needs.</li><li><b>\nExplain technical direction and trade-offs clearly</b> to engineering, product, security, and senior leadership.</li><li><b>\nRepresent the platform team</b> in cross-organization technical forums and build alignment across team boundaries.</li></ul><br>\n<b>\nWho You Are</b><br>\n<ul><li>\nBachelor's degree in Computer Science and 10 years' experience in a related field. The right candidate could also have a different combination, such as a master's degree and 8 years' experience; a Ph.D. and 5 years' experience in a related field; or 22 years' experience in a related field.</li><li>\n10+ years of experience across the software development life cycle, architecture, and platform delivery.</li><li>\nStrong experience designing and delivering cloud-native platforms on AWS, including compute, storage, networking, IAM, observability, and infrastructure automation.</li><li>\nExperience designing, building, or operating AI/ML-powered systems, with exposure to emerging patterns such as RAG, knowledge retrieval, or agent-based architectures.</li><li>\nStrong software engineering fundamentals and working proficiency in Python. Able to design, review, debug, and contribute to Python-based connectors, pipelines, and tooling.</li><li>\nExperience defining evaluation and quality approaches for software, including regression testing, measurable acceptance criteria, HITL review, or other methods suited to non-deterministic systems.</li><li>\nA track record of enterprise integration work across SaaS, data platforms, and operational systems, including API design, authentication (OAuth 2.0, OIDC, SSO), data quality, and reliability concerns.</li><li>\nDemonstrated ownership of a platform or system architecture used by multiple engineering teams.</li><li>\nExperience setting technical direction and influencing decisions across team boundaries without relying on formal authority.</li><li>\nExperience establishing engineering practices and team norms in a newly formed team.</li><li>\nApplicants must be authorized to work in the United States for any employer without current or future sponsorship.</li><li>\nAbility to work in the office three days per week.</li><li>\nWillingness to participate in an on-call rotation and lead incident response for production platform systems.</li></ul><br>\n<b>\nPreferred Qualifications</b><br>\n<ul><li>\nExperience with Amazon Bedrock, AWS managed AI/ML services, or a comparable enterprise AI platform.</li><li>\nExperience working directly with a cloud or platform vendor on product capabilities and roadmap priorities.</li><li>\nFamiliarity with the MCP specification and SDKs, or experience building comparable agent/tool integration layers and governance patterns.</li><li>\nBackground in knowledge graphs, graph databases, or enterprise knowledge and retrieval systems.</li><li>\nExperience with Infrastructure as Code (preferably Terraform) and CI/CD for cloud or AI-enabled systems. Familiarity with GitHub Actions and self-hosted runners is a plus.</li><li>\nExperience with HITL workflows, agent orchestration, evaluation harnesses, or LLM-backed middleware.</li><li>\nExperience with Snowflake, including semantic views or similar semantic-layer technology.</li><li>\nBackground in data loss prevention, PII redaction, or zero-trust data pipelines.</li><li>\nExperience building internal developer platforms, developer tooling, or platform-as-a-product capabilities.</li><li>\nExperience taking ownership of an inherited codebase and bringing it to a supportable, well-documented state.</li><li>\nExperience with event-driven architectures and workflow engines such as AWS Step Functions or Temporal.</li><li>\nExperience establishing spec-driven development, evaluation, governance, or verification practices for AI-assisted engineering.</li><li>\nPrior work in automotive, media, or another large enterprise with a complex system landscape is an advantage but not required.</li><li>\nExperience designing and operating APIs for other teams, including versioning and backward compatibility.</li></ul><br>\nUSD 163,400.00 - 272,300.00<br>\n<br>\n<b>Compensation:</b><br>\n<br>\nCompensation includes a base salary in the range of $163,400.00 - $272,300.00. The base salary may vary within the anticipated base pay range based on factors such as the ultimate location of the position and the selected candidate's knowledge, skills, and abilities. Position may be eligible for additional compensation that may include an incentive program.<br>\n<br>\n<b>Benefits:</b><br>\n<br>\nThe Company offers eligible employees the flexibility to take as much vacation with pay as they deem consistent with their duties, the company's needs, and its obligations; seven paid holidays throughout the calendar year; and up to 160 hours of paid wellness annually for their own wellness or that of family members. Employees are also eligible for additional paid time off in the form of bereavement leave, time off to vote, jury duty leave, volunteer time off, military leave, and parental leave.<br>\n<br>\nEOE, including disability/vets</div>",
        "description_text": "Cox Automotive is deploying enterprise AI capabilities on AWS Quick across the enterprise, helping teams work more effectively in their day-to-day operations. We are seeking a Principal Software Engineer to own technical direction for the platform, the applications, and integrations around it. This role shapes architecture across two products: AWS Quick, an emerging AI platform for agents and enterprise connectors; and an established internal AI Artifact Hub used across Cox Automotive that lets engineering and product teams publish and share interactive artifacts through a web experience and an MCP server interface. This is a hands-on technical leadership role. You will make the key architecture and engineering decisions, lead engineers through implementation, and stay close enough to the code to review designs, debug difficult issues, and contribute where it matters. The team builds connectors, agents, knowledge bases and data pipelines, and quality systems that integrate Quick with Cox Automotive's core enterprise tools and operational systems. This is early-stage work with executive sponsorship, direct access to AWS technical teams, and the autonomy to define the architecture. What You'll Do Architecture & Technical Direction Own the end-to-end architecture for the AWS Quick customization layer, including connectors, agent orchestration, knowledge ingestion, service boundaries, and infrastructure as code. Define the Model Context Protocol (MCP) strategy for enterprise integrations: tool contracts, governance, authentication, authorization, and safety boundaries. Own the technical direction of the AI Artifact Hub. Assess its current state, define a hardening plan, and improve reliability as adoption grows. Make and document major architecture decisions, including the trade-offs behind them. Build for near-term delivery without closing off the platform's next stage. Work with Enterprise Architecture, Security, Cloud Automation, and AWS technical teams to align platform direction and resolve cross-team issues. AI Platform Engineering & Quality Own the quality and evaluation approach for AI capabilities, including automated regression tests, measurable acceptance criteria, and human-in-the-loop (HITL) feedback. Lead technical decisions around enterprise knowledge and retrieval, including ingestion, content curation, metadata, knowledge graphs, retrieval quality, context management, and platform-supported RAG configuration. Define standards and reusable patterns for agents and skills. Set the design for MCP servers, tool interfaces, and structured APIs intended for agent use. Establish spec-driven, verification-first practices for AI-assisted software development. Evaluate new capabilities and adopt them when they improve delivery without weakening quality or security. Resilient Distributed Systems Set production reliability standards for AI-enabled integrations, including monitoring, observability, graceful degradation, and incident response. Define failure and recovery patterns: timeouts, bounded retries with backoff, circuit breakers, and clear fallback behavior. Define idempotency and durable workflow patterns for operations that cross service or third-party boundaries. Lead response to significant platform incidents and drive the follow-up engineering work. Technical Leadership & Influence Lead architecture and design reviews. Pair with engineers on the hardest problems and raise the team's engineering judgment. Guide the team's Python implementation through design, code review, debugging, and targeted production contributions. Serve as a senior technical contact for AWS on platform architecture, product capabilities, and roadmap needs. Explain technical direction and trade-offs clearly to engineering, product, security, and senior leadership. Represent the platform team in cross-organization technical forums and build alignment across team boundaries. Who You Are Bachelor's degree in Computer Science and 10 years' experience in a related field. The right candidate could also have a different combination, such as a master's degree and 8 years' experience; a Ph.D. and 5 years' experience in a related field; or 22 years' experience in a related field. 10+ years of experience across the software development life cycle, architecture, and platform delivery. Strong experience designing and delivering cloud-native platforms on AWS, including compute, storage, networking, IAM, observability, and infrastructure automation. Experience designing, building, or operating AI/ML-powered systems, with exposure to emerging patterns such as RAG, knowledge retrieval, or agent-based architectures. Strong software engineering fundamentals and working proficiency in Python. Able to design, review, debug, and contribute to Python-based connectors, pipelines, and tooling. Experience defining evaluation and quality approaches for software, including regression testing, measurable acceptance criteria, HITL review, or other methods suited to non-deterministic systems. A track record of enterprise integration work across SaaS, data platforms, and operational systems, including API design, authentication (OAuth 2.0, OIDC, SSO), data quality, and reliability concerns. Demonstrated ownership of a platform or system architecture used by multiple engineering teams. Experience setting technical direction and influencing decisions across team boundaries without relying on formal authority. Experience establishing engineering practices and team norms in a newly formed team. Applicants must be authorized to work in the United States for any employer without current or future sponsorship. Ability to work in the office three days per week. Willingness to participate in an on-call rotation and lead incident response for production platform systems. Preferred Qualifications Experience with Amazon Bedrock, AWS managed AI/ML services, or a comparable enterprise AI platform. Experience working directly with a cloud or platform vendor on product capabilities and roadmap priorities. Familiarity with the MCP specification and SDKs, or experience building comparable agent/tool integration layers and governance patterns. Background in knowledge graphs, graph databases, or enterprise knowledge and retrieval systems. Experience with Infrastructure as Code (preferably Terraform) and CI/CD for cloud or AI-enabled systems. Familiarity with GitHub Actions and self-hosted runners is a plus. Experience with HITL workflows, agent orchestration, evaluation harnesses, or LLM-backed middleware. Experience with Snowflake, including semantic views or similar semantic-layer technology. Background in data loss prevention, PII redaction, or zero-trust data pipelines. Experience building internal developer platforms, developer tooling, or platform-as-a-product capabilities. Experience taking ownership of an inherited codebase and bringing it to a supportable, well-documented state. Experience with event-driven architectures and workflow engines such as AWS Step Functions or Temporal. Experience establishing spec-driven development, evaluation, governance, or verification practices for AI-assisted engineering. Prior work in automotive, media, or another large enterprise with a complex system landscape is an advantage but not required. Experience designing and operating APIs for other teams, including versioning and backward compatibility. USD 163,400.00 - 272,300.00 Compensation: Compensation includes a base salary in the range of $163,400.00 - $272,300.00. The base salary may vary within the anticipated base pay range based on factors such as the ultimate location of the position and the selected candidate's knowledge, skills, and abilities. Position may be eligible for additional compensation that may include an incentive program. Benefits: The Company offers eligible employees the flexibility to take as much vacation with pay as they deem consistent with their duties, the company's needs, and its obligations; seven paid holidays throughout the calendar year; and up to 160 hours of paid wellness annually for their own wellness or that of family members. Employees are also eligible for additional paid time off in the form of bereavement leave, time off to vote, jury duty leave, volunteer time off, military leave, and parental leave. EOE, including disability/vets",
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}