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JOB DESCRIPTION
We are scaling artificial intelligence across a global enterprise — moving from experimentation to durable, realized business value. Our AI Transformation Community of Practice (CoP) exists to accelerate the transformations owned by our senior AI Transformation Leads, who (along with our business and function executives) set the aspiration and translate it into measurable business outcomes, shape the transformation portfolio, and enable delivery at pace. The goal of the community is to enable the AI Transformation Leads to reach the right capabilities, expertise, and assets faster, remove blockers, increase reuse across businesses and functions, and move from ambition to execution and value. The community will consist of a flagship summit, recurring deep-dive sessions, and an always-on virtual community. These are the mechanisms; accelerating these leaders is the goal.
As a Vice President, AI Transformation Community of Practice (CoP) – Program Manager in the enterprise AI Skilling & Enablement organization, you own the delivery and operations of the community — ensuring it runs, and runs well. You will anticipate what these leaders need next, structure the ambiguous problems they bring, connect capabilities and people across the organization to help shape answers, and turn that judgment into a crisp operating cadence. You will report directly to the Executive Director, AI Transformation Community of Practice, who holds overall accountability for the community including the content and curriculum agenda. You will partner closely with our strategy teams serving businesses and functions on AI transformation. We will measure you on leader outcomes — faster access to the right capabilities, barriers removed, reuse across businesses and functions, and transformations moving into delivering business value — not on activity.
Job Responsibilities
- Own delivery and operations of the community end to end, partnering with the Executive Director who holds overall accountability for the community and owns the content and curriculum agenda, and drive whatever delivery is needed — capability, people, budget, partners — to make it work for these leaders.
- Structure the ambiguous problems leaders bring — portfolio choices, capability gaps, organization design, controls — into crisp problem statements we can act on, and connect the dots across businesses to mobilize the right people around each.
- Design and own the operating cadence — the summit, the recurring deep-dive sessions, and the operations and moderation of the always-on virtual community — plus the decision forums, escalation paths, and artifacts that turn what leaders surface into action.
- Maintain the authoritative single view of our AI Transformation Leads across businesses and functions: roster, sponsors, active initiatives, and engagement history.
- Build and manage the integrated program plan, milestone map, critical path, and risk and issue register; escalate blockers with clear options and trade-offs.
- Deliver the flagship summit end to end, partnering with our strategy and events colleagues on venue, production, and run of show, and with the Executive Director who owns content architecture, so leaders leave with new answers, connections, and momentum.
- Stand up and then run executive-ready reporting to our senior sponsors on how the community is accelerating leaders: barriers removed, time to capability, cross-business reuse, and transformations moving into value.
- Own the knowledge-capture mechanism across every touchpoint and the routing of what it surfaces: track recurring patterns in leaders' asks and route them to the Executive Director for the content slate, Strategy for the AI Transformation Playbook, and to capability owners for their roadmaps.
- Manage the program budget in partnership with our finance team (plan versus actual, forecast, spend risk) and own vendor management, statements of work, and service-provision oversight.
- Serve as the operating counterpart to our executive development colleagues, so our cadence and logistics integrate cleanly with the broader senior-leader offering.
Required Qualifications, Capabilities, and Skills
- 8 years of experience across senior-audience program, forum, or transformation delivery in a complex enterprise and strategy, consulting, or AI transformation leadership.
- Experience owning an operating cadence for an executive audience, from roster management and run of show through post-event knowledge capture and sharing.
- Experience running program governance, integrated planning, and risk and dependency management, and producing executive-ready reporting.
- Experience preparing decision-ready materials for business heads and senior executives, including status reporting and framing clear asks.
- Experience managing a program budget, including plan versus actual, forecasting, and spend-risk reporting with a finance partner.
- Hands-on experience using generative AI tools in your own work, and a point of view on enterprise AI transformation substantive enough to hold a peer conversation with transformation leaders.
- Experience leading stakeholders across human resources, strategy, finance, events, and business partners on a shared agenda.
- Advanced proficiency with program-management and collaboration tools and executive materials, and willingness to travel up to 20 percent for flagship events.
Preferred Qualifications, Capabilities, and Skills
- Prior experience running a senior-audience community of practice, guild, or forum at scale, or comparable flagship-event ownership.
- Prior partnership with a dedicated executive development function on shared senior-leader offerings.
- Experience integrating a community or forum program with an enterprise playbook or methodology.
- Familiarity with a regulated environment and its governance expectations, with a risk and controls mindset embedded in planning.
ABOUT US
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
Human Resources plays a critical role in driving the employee experience, shaping the firm's culture and building a diverse and inclusive workforce. We are a strategic partner to the business — working with leaders across the firm to hire, develop and retain the best people and help meet business goals. Together, we foster a work environment in which our people are supported, feel like they belong and are able to make an impact through their work. We develop and deliver a suite of products and services that help make JPMorgan Chase an employer of choice and drive our business forward.
Salary Context
This $123K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At JPMorganChase, 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 $211,950 based on 13,048 positions with disclosed compensation. This role's midpoint ($149K) sits 30% below the category median. Disclosed range: $123K to $175K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
JPMorganChase AI Hiring
JPMorganChase has 178 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Product Manager, AI Software Engineer. Positions span San Francisco, CA, US, Jersey City, NJ, US, New York, NY. Compensation range: $155K - $325K.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% below the national 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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