Engineer Manager AI
Posted
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).
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).
