Sr Software Engineer

Cox Enterprises; Austin (TX); On-site; USD 150,000 - 190,000; Full time

Job summary

Cox Automotive is seeking a Senior Software Engineer to design, build, and operate secure software and cloud infrastructure for enterprise AI initiatives on AWS.

You will influence engineering practices, mentor teammates, and collaborate with AWS teams to shape the platform, connectors, and ROI.

This role requires strong Python, AWS, security controls, and IaC skills, with on-site work in Austin three days weekly.

Qualifications

• Bachelor’s degree and 4 years’ experience in a related field, or an equivalent combination.
• 5+ years of professional software development experience, including building and operating production systems.
• Proficiency in Python for APIs, data pipelines, and integrations.

Responsibilities

• Own platform integration infrastructure and authorization controls for MCP servers.
• Collaborate with AWS teams to troubleshoot issues and adopt new capabilities.
• Design and implement observability, cost tracking, and reliability features.
• Define and enforce guardrails, security reviews, and production readiness.

Skills

Python

AWS

APIs

CI/CD

Terraform

Distributed systems

Security policies

Authorization

Communication

On-call

Education

Bachelor's degree

Tools

GitHub Actions

OAuth 2.0

Entra

Job description

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.