Principal Software Engineer

Company Cox Automotive - USA
Job Family Group Engineering / Product Development
Job Profile Principal Software Engineer
Management Level Director
Flexible Work Option Hybrid - Ability to work remotely part of the week
Travel % Yes, 5% of the time
Work Shift Day
Compensation Compensation includes a base salary in the range of $ - $. 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.

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 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…