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About This Role
JOB DESCRIPTION
The Chief Data \& Analytics Office (CDAO) Product GenAI Transformation \& Development VP carries two core mandates — driving the transformation of the product function to be GenAI/Agent\-first, and developing and maintaining the scripts, tools, and lightweight applications that make the organization measurably faster and more effective. GenAI has fundamentally changed what a small, skilled team can build and how quickly they can build it. What previously required weeks of engineering effort can now be prototyped in hours. This role exists to capitalize on that shift — driving accelerated adoption of AI\-native workflows across the product organization, delivering tangible productivity gains from internal tooling, and systematically eliminating manual effort that no longer needs to be manual. Success is measured by the speed of AI adoption, the reliability and impact of internal tools, and sustained operational improvement across the product function.
As a CDAO Product AI Transformation \& Development VP in CDAO Product \& Platforms, you will partner with Product Managers, Engineers, and leadership to reshape how the product function operates — embedding GenAI and agentic approaches into daily workflows, decision\-making, planning, and delivery, and moving the organization from occasional AI experimentation toward a genuinely AI\-native operating posture where intelligent automation is the default rather than the exception. This is transformation driven by working solutions — building functional tools, demonstrating measurable results, and making the case for new ways of working through tangible outcomes rather than theoretical frameworks. You also own the internal tooling layer that makes the product organization faster, leaner, and more effective. This includes designing, building, and maintaining practical scripts, automations, and lightweight applications that eliminate toil, streamline operations, and improve team effectiveness. You act as the bridge between strategic AI vision and working code, turning transformation ambitions into deployed solutions that teams rely on every day.
Job Responsibilities:
AI Transformation \& Adoption\-
- Define and execute the roadmap for transforming the product function to be GenAI/Agent\-first, establishing clear phases, milestones, and success criteria that move the organization from current state to sustained AI\-native operations.
- Identify high\-impact use cases where GenAI and agentic solutions can replace or augment manual product management workflows — including intake triage, backlog synthesis, stakeholder reporting, and dependency analysis — prioritizing based on effort, value, and feasibility.
- Design adoption strategies including training curricula, change management plans, playbooks, and success metrics that drive sustained behavioral change rather than one\-time pilots that fade after launch.
- Stay current on emerging GenAI and agentic capabilities and translate them into practical opportunities for the product function, serving as the organization's internal expert on what is possible, what is maturing, and what is not yet ready for enterprise use.
Internal Tools \& Development\-
- Design, build, and maintain scripts, automations, and lightweight applications that improve organizational efficiency — including automated reporting pipelines, data aggregation tools, workflow orchestrators, and integrations.
- Identify repetitive manual processes across the product organization and develop programmatic solutions to eliminate or substantially reduce them, prioritizing based on time savings, error reduction, and user impact.
- Manage the full lifecycle of internal tools including requirements gathering, development, testing, deployment, documentation, and ongoing maintenance, ensuring each tool remains reliable and fit for purpose as the organization evolves.
Required Qualifications, Skills, and Capabilities:
- 5\+ years of experience in a role combining technology delivery, AI/ML applied work, product operations, or technical program management, with demonstrated ability to operate across both strategic and hands\-on technical responsibilities.
- Demonstrated experience developing automations, scripts, and lightweight applications using GenAI tooling — including AI\-assisted code generation, copilot\-style development environments, and agentic development workflows — with the ability to rapidly build and ship internal solutions that teams rely on daily.
- High ownership, independence, and comfort operating in ambiguous, fast\-moving environments — able to make progress without detailed requirements or heavy oversight, and willing to proactively identify and act on opportunities for improvement.
- Demonstrated change leadership and the ability to drive adoption of new ways of working across teams that may be skeptical or entrenched, combining persistence with clear communication of the value at stake.
- Experience with modern development and collaboration tools including Git, CI/CD pipelines, Atlassian, Terraform, and cloud platforms, with comfort operating across the full development lifecycle from prototyping through deployment.
Preferred Qualifications, Skills, and Capabilities:
- Experience in management consulting, corporate strategy, or organizational transformation with an AI or digital focus, providing a structured approach to driving change at scale.
- Hands\-on development skills in Python or similar scripting languages, with demonstrated ability to build and maintain production\-quality scripts and internal applications.
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.
Salary Context
This $142K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 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 $178,940 based on 11,900 positions with disclosed compensation. This role's midpoint ($166K) sits 7% below the category median. Disclosed range: $142K to $190K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
JPMorganChase AI Hiring
JPMorganChase has 68 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Engineering Manager, AI Product Manager. Positions span Jersey City, NJ, US, Tampa, FL, US, New York, NY, US. Compensation range: $130K - $325K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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