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About This Role
Accelerate your career. Join the organization that's driving the world's technology and shape the future.
Ingram Micro is a leading technology company for the global information technology ecosystem. With the ability to reach nearly 90% of the global population, we play a vital role in the worldwide IT sales channel, bringing products and services from technology manufacturers and cloud providers to business-to-business technology experts. Our market reach, diverse solutions and services portfolio, and digital platform Ingram Micro Xvantage™ set us apart. Learn more at www.ingrammicro.com
Come join our team where you’ll make technology happen in surprising ways. Let’s shape tomorrow - it’ll be a fun journey!
This position is located in our Irvine, California corporate headquarters on a hybrid schedule.
About the Role:
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.
Where This Role Fits:
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.
Responsibilities:
Team Leadership & Development
- 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.
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Agent Delivery & Portfolio Management
- 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.
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Platform Alignment & Operations
- 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.
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Required Qualifications:
- 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.
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Preferred Qualification s
- 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.
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#LI-Hybrid
The typical base pay range for this role across the U.S. is USD $158,900.00 - $270,100.00 per year. The ranges above reflect the potential annual base pay across the U.S. for all roles; the applicable base pay range will depend on the candidate’s primary work location, pay grade, and variable compensation plan. Individual base pay within each range depends on various factors, in addition to primary work location, such as complexity and responsibility of role, job duties/requirements, and relevant experience and skills. Base pay ranges are reviewed and typically updated each year. Offers are made within the base pay range applicable at the time of hire. New hires starting base pay generally falls in the bottom half (between the minimum and midpoint) of a pay range.
At Ingram Micro certain roles are eligible for additional rewards, including merit increases, annual bonus or sales incentives and long-term incentives. These awards are allocated based on position level and individual performance. U.S.-based employees have access to healthcare benefits, paid time off, parental leave, a 401(k) plan and company match, short-term and long-term disability coverage, basic life insurance, and wellbeing benefits, among others.
This is not a complete listing of the job duties. It’s a representation of the things you will be doing, and you may not perform all these duties.
Please be prepared to pass a drug test and successfully pass a pre-employment (post offer) background check.
Ingram Micro Inc. is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other protected category under applicable law.
Salary Context
This $158K-$270K range is above the 75th percentile for AI Engineering Manager roles in our dataset (median: $202K across 10 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 11,520 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At Ingram Micro, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $253,461 based on 58 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($214K) sits 15% below the category median. Disclosed range: $158K to $270K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and Research Engineer ($252,000). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Ingram Micro AI Hiring
Ingram Micro has 3 open AI roles right now. They're hiring across AI Engineering Manager, AI/ML Engineer. Positions span Irvine, CA, US, Irvine, CA. Compensation range: $226K - $270K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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