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JOB DESCRIPTION
Join us as a Senior Product Transformation \& Agentic AI Lead and help shape the future of product teams in an AI\-enabled environment. This is your opportunity to drive meaningful change, accelerate innovation, and make a lasting impact on how we deliver value. You will work alongside talented professionals, championing agentic AI adoption and transformation. If you are passionate about technology and continuous improvement, this role offers a unique platform to lead and inspire. Be part of a team that is redefining how we work and grow.
As a Senior Product Transformation \& Agentic AI Lead in Consumer and Community Banking Operations, you will play a central role in transforming how product teams operate. You will help us translate emerging AI capabilities into practical solutions, fostering a culture of experimentation and continuous improvement. Together, we will build inclusive, innovative teams that deliver measurable results and drive sustainable change.
*Consumer \& Community Banking (CCB) Operations Product organization provides critical cross line of business support across JPMC and serves as one of the largest product portfolios in CCB, delivering value to millions of customers and over 30,000 employees. As a product team member, your problem\-solving skills will place you on the cutting edge of defining the vision, creating the strategy, and building the roadmap to solutions that impact millions. Along the way, you'll develop a deep, end\-to\-end understanding of the business and find an inclusive culture that welcomes diverse ideas and supports your individual growth and career mobility.*Job responsibilities* Lead agentic AI adoption across product teams by training, coaching, and guiding day\-to\-day use of AI\-enabled workflows in discovery, planning, delivery, and continuous improvement.
- Operate as an embedded transformation leader within product teams, helping redesign how work gets done.
- Identify opportunities for agentic AI to remove friction, accelerate learning, improve decision quality, and increase team throughput while maintaining strong governance and risk awareness.
- Work hands\-on with teams to prototype, test, and implement new practices, prompts, automations, and operating model patterns.
- Translate technical AI concepts into practical behaviors, playbooks, and learning paths for product, engineering, design, and business partners.
- Guide leaders and teams through the people, process, and operating model shifts required to scale AI adoption responsibly.
- Establish feedback loops that measure adoption, effectiveness, and transformation outcomes, focusing on capability uplift, workflow improvement, and business impact.
- Partner with senior leadership to shape the future\-state product operating model and embed agentic AI capabilities across the domain.
- Promote a culture of experimentation, learning, and continuous improvement by helping teams build confidence, judgment, and discipline in AI\-assisted work.
Required qualifications, capabilities, and skills* 8 years of experience across product delivery, transformation, technology, or digital operating model change in complex environments.
- Demonstrated success leading cross\-functional teams through meaningful changes in ways of working, with the ability to influence leaders, practitioners, and stakeholders at multiple levels.
- Strong technical acumen, including the ability to work credibly with engineers, understand modern software delivery practices, and apply AI tools in practical team settings.
- Hands\-on experience using generative AI, automation, copilots, agentic workflows, or related tooling to improve team outcomes, workflow quality, or operational efficiency.
- Deep understanding of the product development lifecycle, including strategy, discovery, planning, design, implementation, testing, deployment, operations, and continuous improvement.
- Ability to teach, coach, and facilitate in a way that builds capability through direct partnership and observable practice.
- Strong communication, storytelling, and change leadership skills, with the ability to make emerging concepts concrete and actionable for diverse audiences.
- Experience defining measurable transformation outcomes and using qualitative and quantitative signals to refine adoption strategies.
Preferred qualifications, capabilities, and skills* Background in software engineering, product engineering, architecture, developer enablement, or another technical discipline that supports hands\-on credibility with delivery teams.
- Experience designing or scaling AI enablement programs, transformation playbooks, communities of practice, or enterprise adoption models.
Working knowledge of prompt design, workflow orchestration, human\-in\-the\-loop controls, evaluation approaches, and responsible AI guardrails.
- Experience operating in regulated or large\-scale enterprise environments where technology change must align with risk, control, compliance, and operational readiness requirements.
- Strong facilitation and workshop design skills across strategy sessions, operating model design, learning labs, and team\-based transformation work.
- Certifications or formal training in AI, machine learning, cloud AI platforms, automation technologies, product management, design thinking, change management, digital transformation, or agile/lean methodologies.
- Preferred experience supporting more than one CCB Operations Function/Line of Business.
ABOUT US
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
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.
Equal Opportunity Employer/Disability/Veterans
ABOUT THE TEAM
Our Consumer \& Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most\-used digital solutions – all while ranking first in customer satisfaction.
Operations teams develop and manage innovative, secure service solutions to meet clients' needs globally. Developing and using the latest technology, teams work to deliver industry\-leading capabilities to our clients and customers, making it easy and convenient to do business with the firm. Teams also drive growth by refining technology\-driven customer and client experiences that put users first, providing an unparalleled experience.
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 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 $218,750 based on 3,817 positions with disclosed compensation.
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.
JPMorganChase AI Hiring
JPMorganChase has 88 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, Columbus, OH, US, New York, NY, US. Compensation range: $130K - $325K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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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