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
• LangChain, LlamaIndex, 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).