Interested in this AI/ML Engineer role at Project Management Institute?
Apply Now →About This Role
Are you a dreamer, innovator, thinker and a do\-er who believes in contributing to something that makes difference?
Pursue your passion. Join PMI.
How You’ll Make a Difference
You will lead the execution of PMI’s enterprise AI delivery and governance capability, translating enterprise AI priorities into an actionable, governed portfolio of initiatives that deliver measurable business outcomes. Moving AI efforts from strategy through delivery, adoption, and value realization—ensuring investments scale beyond pilots and are embedded into the way work gets done. Providing end\-to\-end leadership for PMI’s AI portfolio, overseeing program governance, cross\-functional dependencies, risks, and benefits realization, while directly leading enterprise AI initiatives and shared capabilities that serve multiple functions. You will establish and maintain the governance framework, delivery standards, and reporting structures that support AI work across PMI.
What You’ll Bring to the Role
- Bachelor’s degree in business, project management, information systems, computer science, engineering, analytics, data science, operations, or a related field (required).
- Proficiency in the business language for PMI (English) (required).
- 7\+ years of project, program, or transformation management experience, including enterprise\-scale cross\-functional delivery.
- 5\+ years leading AI, analytics, data, automation, or digital transformation initiatives.
- Demonstrated experience in roadmap planning and execution, governance and risk management, and benefits realization/value tracking.
- Experience translating strategic objectives into integrated delivery plans with clear business outcomes, stakeholder alignment, dependency management, and executive\-ready reporting.
- Experience supporting senior governance forums or steering committees by preparing materials, maintaining decision and action logs, and coordinating follow\-through across multiple owners.
- Experience working effectively in matrixed organizations and leading across multiple teams toward a shared value agenda.
- Strong project/program management across scope, schedule, budget, risk, governance, and executive reporting
- Working knowledge of AI delivery concepts, including use case prioritization, data readiness, model lifecycle considerations, deployment dependencies, and AI\-related risk
- Strong business acumen with the ability to connect delivery decisions to measurable value, adoption, and operational impact
- Proven ability to lead or coordinate a network of project managers across a matrix or PMO and consolidate multi\-workstream status into executive\- and board\-ready portfolio reporting
- Ability to operate effectively in ambiguous, emerging\-technology environments while maintaining structure, momentum, and accountability
- Excellent communication, facilitation, presentation, and stakeholder management skills, including experience supporting senior executives and steering forums
- Experience with agile or hybrid delivery methods and the ability to tailor ways of working to AI initiative maturity and risk profile
- PMP required; PMI\-CPMAI™ and/or PMI\-ACP strongly preferred; responsible AI or AI project management training preferred.
What you can expect from us
We value and nurture an environment of inclusivity and diversity, and a culture of communication and collaboration. Your health, safety and well\-being come first, and we believe that you should have time for your work, but you should also have time for you..
Join us and you’ll get:
- an excellent total package, with compensation and benefits based upon your geographic location.
- skill development opportunities, to help you grow now and into the future.
- access to a global network, to enrich your professional experience.
- flexible options to help balance work time and your time
- award and bonus opportunities.
The salary offer will be based on several factors, including the candidate’s demonstrated skills, qualifications and relevant experience.
Let’s help make the world work better for everyone. Apply today!
Project Management Institute, Inc. is committed to providing equal employment opportunities without regard to sex, gender identity, sexual orientation, race, color, religious creed, national origin, physical or mental disability, protected veteran status, or any other characteristic protected by applicable law.
Salary Context
This $159K-$195K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Project Management Institute, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($177K) sits 19% below the category median. Disclosed range: $159K to $195K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Project Management Institute AI Hiring
Project Management Institute has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $195K - $195K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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