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JOB DESCRIPTION
As a Product Risk – AI Risk Transformation – Managing Director in the Asset and Wealth Management Risk Product Management team you will be part of a team at the frontier of risk management transformation. This is a unique opportunity to shape the strategy, operating model, and delivery of an AI\-native, agentic platform that reimagines risk oversight — moving toward a continuously reasoning, intelligent capability. Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo, and striving to be best\-in\-class.
The position is a senior leadership role responsible for:
- Setting the strategy direction and leading execution for a portfolio of risk products and platforms across Asset and Wealth Management Risk.
- Defining the vision, integrated roadmap, product operating model, and success measures for AI\-enabled risk transformation, ensuring product delivery is aligned to business priorities, risk management standards, regulatory expectations, and long\-term enterprise objectives.
- Leading cross\-functional product teams, partnering closely with senior leaders across Risk, Technology, Quantitative Analytics \& Data Management, Data \& Analytics, Asset \& Wealth Management and Wholesale Credit Risk, and business stakeholders
- Driving disciplined execution across modernization, simplification, governance and controls uplift, transparency, user experience, AI\-enabled PDLC, and regulatory compliance.
Job Responsibilities
- Set the strategic vision, product direction, and multi\-year roadmap for AI Risk Transformation across a portfolio of Asset \& Wealth Management Risk products and platforms.
- Own and be accountable for the full product management lifecycle, including vision, roadmap, discovery, delivery, risk management, value realization, adoption, controls alignment, and measurable business outcomes.
- Lead portfolio prioritization across product/platform areas, ensuring investments are aligned to strategic themes such as modernization, simplification, governance and controls uplift, transparency, user experience, AI\-enabled delivery, and regulatory compliance.
- Translate enterprise and senior stakeholder priorities into an integrated roadmap, clear product objectives, key results, success metrics, and disciplined execution plans.
- Lead, coach, mentor, and empower product owners and cross\-functional delivery teams to execute consistently across the product lifecycle while embedding scalable product management best practices.
- Drive strategic alignment across Risk, Technology, Quantitative Analytics \& Data Management, Data \& Analytics, Asset \& Wealth Management and Wholesale Credit Risk, and senior business stakeholders.
- Act as the senior escalation point for cross\-team dependencies, delivery risks, scope trade\-offs, resourcing constraints, governance commitments, and regulatory priorities.
- Sponsor release planning and execution across the portfolio, ensuring production readiness, communication, training, documentation, controls, and auditability are embedded into delivery.
- Define and oversee product performance metrics, delivery health indicators, adoption measures, user experience signals, and value realization reporting to improve transparency and decision\-making.
- Champion the voice of the end user across the product portfolio by driving customer discovery, journey mapping, usability feedback loops, and continuous improvement.
- Embed AI\-enabled delivery practices into the product development lifecycle where appropriate, ensuring responsible use of tools to improve requirements quality, testing efficiency, documentation, and product outcomes while adhering to firm policies and regulatory expectations.
Required Qualifications, Capabilities, and Skills
- A minimum of 15 years of experience or equivalent expertise in product management, product strategy, business transformation, risk management, or technology delivery within financial services or another highly regulated environment.
- Demonstrated experience leading high\-impact, large\-scale products, platforms, or transformation programs across complex, cross\-functional organizations.
- Strong expertise in product management disciplines, including vision setting, roadmap development, discovery, prioritization, value management, requirements definition, delivery execution, and performance measurement.
- Proven ability to set strategy, drive change, and influence senior stakeholders across business, risk, data, technology, controls, and governance functions.
- Deep understanding of risk management processes, operating models, controls, and regulatory expectations, with the ability to translate strategic risk priorities into scalable product capabilities.
- Strong leadership experience, including the ability to lead, coach, and develop product owners or cross\-functional teams.
- Excellent executive communication skills, with the ability to simplify complex concepts, frame decisions, and drive alignment across competing priorities.
- Strong delivery discipline, including experience overseeing Agile execution, release planning, dependency management, UAT, operational readiness, and controls documentation.
- Demonstrated ability to define product success measures, adoption metrics, and delivery health indicators and use data to inform prioritization and continuous improvement.
Preferred Qualifications, Capabilities, and Skills
- Experience supporting Asset \& Wealth Management and/or risk stripes such as Credit Risk, Investment Risk, Market Risk, Counterparty Risk, Fiduciary Risk, or Reputation Risk.
- Experience leading product transformation, operating model implementation, or modernization efforts across large product/platform portfolios.
- Familiarity with data\-intensive platforms, analytics workflows, AI/ML\-enabled capabilities, and partnerships with Quantitative Analytics \& Data Management and Data \& Analytics teams.
- Experience using AI\-enabled tools and practices to improve product delivery, requirements quality, testing, documentation, and PDLC effectiveness.
- Comfort operating in highly regulated environments with formal governance, auditability, controls, and regulatory commitments.
- Proficiency with JIRA or similar tooling, product documentation practices, roadmap management, and executive\-level product reporting.
- Recognized product leader with a track record of driving innovation, transformation, and measurable outcomes across complex organizations.
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 $260K-$450K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($355K) sits 65% above the category median. Disclosed range: $260K to $450K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
JPMorganChase AI Hiring
JPMorganChase has 141 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Jersey City, NJ, US, New York, NY, US, Seattle, WA, US. Compensation range: $120K - $450K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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