Engineer Manager - AI

Engineer Manager - AI

Location:

Irvine, California Salary:
*** K/Y

Hands-on Engineering Manager to lead AI Platform and GenAI product engineering initiatives.

Key Responsibilities:

• Lead delivery execution for AI Platform initiatives
• Contribute directly to AI architecture, coding, evaluations, and deployment
• Build scalable AI-powered systems and workflows
• Mentor AI engineers and drive technical excellence
• Establish AI-native SDLC standards and best practices

Required Technical

Skills:

• Python expertise
• LLM applications, RAG systems, and agent frameworks
• Lang Chain, Llama Index, vector databases
• AI evaluations, prompt engineering, AI observability
• MLOps and AI deployment practices

Required Leadership

Skills:

• 10+ years in software engineering, including 2+ years in a lead/manager role.
• AI-driven SDLC:
  Hands-on experience delivering production code using tools like Copilot, Claude Code, or Cursor; able to articulate workflow impact and guardrails. Coach teams on AI-driven SDLC practices (agentic coding, prompt engineering, automated testing, eval-driven quality).
• Full-stack orientation:
  Comfortable across frontend, backend, and data layers.
• End-to-end ownership:
  Proven delivery from scoping through release and retrospectives.
• Engineering excellence:
  Strong practices in code quality, testing, and CI/CD for production-grade delivery. Monitor and improve team health, velocity, and code quality benchmarks.
• Capture and share learnings, architectural decisions, and process improvements.
• People leadership:
  Experience hiring, mentoring, and providing direct feedback.
• Communication:
  Clear, effective collaboration across product, design, and engineering. Proactively communicate progress, risks, and decisions to VP Engineering and stakeholders

Nice to Have:

• Experience in e-commerce, retail tech, or high-scale consumer platforms.
• Managing distributed/nearshore teams (US, Mexico, LATAM, Asia).
• Contributions to developer tooling, platforms, or SDLC improvements at scale.
• Exposure to MLOps or model deployment pipelines.
• Strong documentation practices (RFCs, architecture decisions, wikis)