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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!
We are seeking an enthusiastic and promising Associate Agentic AI Engineer to join our growing AI CoE. This is an excellent opportunity for an early\-career engineer passionate about artificial intelligence and autonomous systems. In this role, you will contribute to the development, testing, and deployment of AI agents designed to perceive their environment, make decisions, and take actions. You will work under the guidance of senior engineers and architects, collaborating with Data Scientists and business stakeholders to build foundational components of innovative agentic solutions. This role offers a unique chance to learn and grow in the cutting\-edge field of Agentic AI while contributing to impactful projects.
Your role:
AI Agent Development \& Support:
· Assist in the development and implementation of AI agent components using established agentic AI frameworks (e.g., Google ADK, LangChain, AutoGen, CrewAI) and programming languages (primarily Python), under the guidance of senior team members.
· Support the integration of AI agents with enterprise systems, APIs, and data sources.
· Contribute to the development of core agent functionalities, such as natural language processing, basic reasoning with LLMs, and tool usage.
· Write clean, well\-commented, and testable code with a focus on learning and applying best practices.
Collaboration \& Learning:
· Work closely with senior engineers and AI Architects to understand technical designs and implement specific modules or features of AI agents.
· Collaborate with Data Scientists to help integrate data and model outputs into agent workflows.
· Actively participate in agile development processes, including sprint planning, daily stand\-ups, and sprint reviews, focusing on learning and contribution.
· Engage in code reviews (both giving and receiving feedback) to improve code quality and learn from experienced team members.
Testing \& Troubleshooting:
· Assist in developing and executing test cases for AI agent components, including unit tests and integration tests.
· Help troubleshoot and debug issues related to agent behavior and performance under supervision.
· Contribute to the documentation of agent functionalities and testing procedures.
Continuous Growth \& Skill Development:
· Demonstrate a strong commitment to learning and staying updated with advancements in agentic AI, LLMs, machine learning, and software engineering.
· Actively seek opportunities to expand technical skills and knowledge within the AI CoE.
· Contribute to a positive and collaborative team environment.
What you bring to the role:
· Bachelor’s degree in computer science, artificial intelligence, machine learning, software engineering, or a related technical field.
· 0\-2\+ years of experience in software engineering, with a demonstrated interest and foundational knowledge in AI/ML development (internships, academic projects, or personal projects are valuable).
· Basic understanding of or keen interest in Agentic AI, autonomous agents, and Large Language Models (LLMs). Some practical exposure is a plus, which could include Familiarity with or initial exploration of agentic AI concepts or frameworks (e.g. ADK, LangChain, AutoGen, or similar through coursework or self\-study). Understanding of how LLMs (e.g., Gemini, GPT series, Claude, Llama models) can be used for tasks like text generation, understanding, or simple reasoning.
· Proficiency in Python and familiarity with common AI/ML libraries (e.g., scikit\-learn, Pandas, NumPy).
· Solid grasp of fundamental software engineering principles and best practices (e.g., version control with Git).
· Basic understanding of API concepts and how to interact with them.
· Familiarity with data structures and algorithms.
· Exposure to cloud computing platforms (e.g., AWS, Azure, GCP including Google Vertex AI) is a plus.
· Strong problem\-solving aptitude and a willingness to learn.
· Good communication skills and ability to work effectively as part of a team.
Preferred Qualifications \& Experience:
· Master's degree in a relevant field.
· Internship experience focused on AI/ML development.
· Experience with building small projects using LLM APIs.
· Contributions to open\-source projects or active participation in AI/ML communities.
· Familiarity with basic MLOps concepts.
· Exposure to containerization technologies (e.g., Docker).
· A portfolio of projects (e.g., GitHub) demonstrating coding skills and AI/ML interest.
The typical base pay range for this role across the U.S. is USD $83,600\.00 \- $133,800\.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 $83K-$133K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880. This role's midpoint ($108K) sits 40% below the category median. Disclosed range: $83K to $133K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
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: $133K - $133K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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