Manager, Agentic AI Engineering

Ingram Micro•Irvine, CA

3d•$158,900 - $270,100•Hybrid

About The Position

We are seeking a hands-on Manager, Agentic AI Engineering to lead a team of approximately 10 early-career Agent Builders within the AI Center of Excellence. The team delivers a portfolio of AI agents that ranges from internal AICOE tools, to associate-facing productivity agents, to customer-facing solutions. This is a player-coach role. You will grow the team's engineering maturity while delivering production-grade agents on the AICOE Agentic Platform, applying the right level of rigor to each agent based on who uses it and the risk it carries. AICOE operates a hub-and-spoke model: the AICOE hub provides the Enterprise Agentic Platform, architecture standards, governance, and AI Ops, while this team and domain teams across the business build and run agents on top of it. You will partner closely with AICOE Platform, Architecture, and AI Ops, as well as business owners across the organization, and you will report to the Head of AICOE.

Requirements

Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, or a related field (Master's preferred).
8+ years of software engineering experience.
3+ years of engineering management experience leading technical teams.
Proven experience managing and developing junior or early-career engineers.
1-2+ years of hands-on experience shipping GenAI, LLM, or agentic solutions to production.
Experience evaluating and testing LLM or agent systems (evaluation sets, regression testing, quality metrics).
Strong Python skills, with the ability to credibly review code and designs.
Working knowledge of APIs, distributed systems, vector databases, and RAG architectures.
Experience with at least one major cloud platform (GCP, AWS, or Azure).
Strong communication and stakeholder management skills, including with non-technical business owners.

Nice To Haves

Proficiency with Google Cloud AI services (Vertex AI, Gemini models, embeddings, Agent Engine) and experience running production workloads; equivalent experience on AWS or Azure is acceptable.
Experience with Model Context Protocol (MCP) and tool/integration patterns for agents.
Experience with agent frameworks such as ADK, LangChain/LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar.
Experience delivering customer-facing AI solutions with associated safety, governance, and compliance requirements.
Experience with AI observability, guardrails, and production operations.
Domain experience in sales, customer service, supply chain, logistics, or operational automation.
Experience leading delivery across multiple concurrent projects in an enterprise environment.

Responsibilities

Lead, coach, and develop a team of ~10 early-career Agent Builders (0–3 years of experience).
Raise engineering maturity through regular code reviews, design reviews, pairing, and structured individual growth plans.
Establish strong team habits: version control, testing, CI/CD, documentation, and production support.
Identify and grow future technical leads from within the team.
Own hiring, onboarding, performance management, and career development for the team.
Foster a culture of ownership, curiosity, accountability, and engineering excellence.
Reduce dependency on individual developers through documentation, reusable components, and shared knowledge.
Manage a portfolio of agents delivered in parallel, including intake, prioritization, estimation, sprint planning, and status reporting.
Act as the primary delivery partner to business owners: clarify requirements, set realistic expectations, and communicate progress and trade-offs.
Guide the design, build, and deployment of agentic and multi-agent solutions, including LLM-powered applications, RAG, tool/MCP integrations, and orchestration.
Stay technically close to the work: review designs and code, unblock builders, and step in hands-on when needed.
Apply AICOE platform and architecture standards, and ensure every agent meets the risk-tier, quality, and release requirements defined by the AICOE Governance stream.
Provide delivery feedback to the Platform and Architecture teams to help evolve standards, reusable components, and tooling.
Partner with AI Ops to monitor deployed agents for performance, reliability, cost, and user feedback, and drive continuous improvement.