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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!
ABOUT THE OPPORTUNITY
Ingram Micro is the largest IT distributor in the world and one of the largest Microsoft distribution partners globally. Its Forward-Deployed Engineering practice works embedded with customers to turn priority AI use cases into production solutions, then packages what works so partners can deliver it across markets.
The Forward-Deployed Principal Services Engineer designs, builds, tests, and deploys AI agents and applications using Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, and related Microsoft cloud services. Working within the reference architectures, engineering standards, and reusable components set by the practice, the role ships production software rather than proofs of concept and works end to end from discovery through deployment and handoff. It serves as the in-house expert on the Microsoft agent stack for the engagements it leads.
The role works closely with the Senior Principal Services Architect, the Microsoft Practice Leads, and system integrator partners, and with business, technical, and regional stakeholders to turn prioritized use cases into secure, production-ready solutions. As the practice grows, the role offers a path to senior principal responsibilities, including setting the standards the practice builds on.
YOUR ROLE
- Engineer production agents with customers using the practice's shared templates, components, and tooling, following standard build patterns so every solution is built the same way
- Build agents on the Microsoft frontier stack and select appropriate models from the Microsoft Foundry model catalog for each workload
- Prototype, validate architectures, and test approaches in real customer environments, and harden agents for production
- Apply security, data-boundary, and performance standards to every solution, including managed agent identity and guardrails
- Implement grounding and retrieval with Foundry IQ, integrate tools through Toolboxes, Model Context Protocol, and connectors, and build memory, tool use, and multi-step workflows
- Deploy the same solution across customers through standard pipelines, and publish and govern agents in Microsoft 365 Copilot, Teams, and Agent 365
- Lead time-bound customer engagements, typically measured in weeks, with defined handoff and enablement milestones
- Work directly with customers and partners to understand their business priorities, use cases, and gaps, and translate them into solution designs
- Contribute reusable agents, workflows, and components to the practice library, built once and reused across customers
- Contribute to packaging mature agents into marketplace-ready offers and to enablement content for system integrator partners
- Co-deliver with system integrator partners and support them until they can deliver independently
- Share field feedback with the Senior Principal Services Architect to refine reference architectures and standards
Coach and review the work of other engineers and partner delivery teams
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REQUIREMENTS
- Bachelor's degree in computer science or a related field, or equivalent experience
- 8+ years in software engineering, solutions architecture, or technical consulting, including 5+ years building enterprise cloud or AI solutions
- Customer-facing delivery experience, with a record of shipping production software
- Experience working with global or regional teams; travel to customer sites as required
Strong written and verbal communication skills, with the ability to explain technical designs to business and technical stakeholders
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The typical base pay range for this role across the U.S. is USD $133,400.00 - $226,800.00 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 $133K-$226K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Ingram Micro, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($180K) sits 15% below the category median. Disclosed range: $133K to $226K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Ingram Micro AI Hiring
Ingram Micro has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Irvine, CA, US. Compensation range: $226K - $226K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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