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About This Role
Are you a dreamer, innovator, thinker and a do-er who believes in contributing to something that makes difference?
Make an impact. Join PMI.
How will you make a difference to PMI?
We are seeking a senior-level AI Engineer, Marketing to lead the design, development, and operationalization of AI-powered workflows across the marketing organization. This role serves as the technical owner of Marketing AI and Automation initiatives, partnering closely with Marketing stakeholders and the Martech team to deliver production-ready, scalable AI capabilities embedded within existing tools and processes.
The AI Engineer owns the end-to-end lifecycle of marketing AI solutions—from opportunity intake and backlog management through architecture, deployment, and measurement—ensuring solutions are reliable, observable, and aligned with business outcomes.
What you’ll bring to the role:
AI Product & Backlog Ownership
- Own and manage the Marketing AI & Automation backlog, translating marketing opportunities into prioritized initiatives.
- Partner with Marketing Operations and Martech teams to identify high-impact AI use cases.
- Define success metrics and acceptance criteria for AI solutions.
- Serve as technical owner balancing rapid iteration with production stability.
AI Systems & Agentic Workflow Design
- Design and build LLM-powered workflows and multi-agent AI systems.
- Implement RAG pipelines using enterprise data and knowledge sources.
- Create reusable AI components embedded into existing marketing tools.
- Ensure systems include guardrails, fallback behaviors, and human-in-the-loop controls.
Data Foundation & Platform Enablement
- Build the data foundation required for AI agents across CRM, analytics, and data warehouse environments.
- Enable AI functionality within existing Martech platforms.
- Partner with analytics teams to ensure data quality and accessibility.
- Document data flows, integrations, and dependencies.
Production Deployment & Operations
- Deploy AI systems into production with monitoring and evaluation.
- Implement observability to track performance and business impact.
- Continuously improve AI solutions based on usage and feedback.
- Ensure systems are secure, supportable, and resilient.
Stakeholder Partnership & Enablement
- Act as a trusted AI partner to Marketing leadership.
- Translate complex AI concepts into business implications.
- Enable non-technical teams through intuitive tools and documentation.
- Maintain transparency on roadmap, risks, and outcomes.
Role Qualifications:
Qualifications
- Senior-level experience delivering AI-powered systems in enterprise environments.
- Hands-on experience with LLM workflows, RAG, and agentic systems.
- Strong analytics or data engineering background.
- Proven cross-functional marketing and technology partnership skills.
Skills & Competencies
- AI systems and agentic workflow design
- Marketing automation and CRM workflows
- Analytics and evaluation frameworks
- Product mindset and backlog prioritization
- Clear communication with technical and non-technical audiences
Technology Environment
- CRM: Adobe, Microsoft Dynamics 365
- Analytics: Adobe Analytics, Tableau
- Data Warehouse: Snowflake
- Work Management: Jira, monday.com
- Collaboration: SharePoint, Microsoft Teams
Travel Requirements
Remote role with less than 10% travel as needed
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 $153K-$186K range is below 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 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 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 ($169K) sits 20% below the category median. Disclosed range: $153K to $186K.
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.
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: $186K - $186K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 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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