Sr Software Engineer

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.

• 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, Service Now, 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.

• 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.

• 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.

• 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.

• 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, Cloud Watch, Step…