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About This Role
JOB DESCRIPTION
This is a rare opportunity to sit at the intersection of artificial intelligence and Finance transformation — not just supporting the strategy, but building it with your own hands. You'll architect the next generation of Artificial Intelligence-driven analytics and decision-support products for one of the world's leading asset and wealth management businesses. If you thrive on shipping products that stay ahead of the curve and solving the hardest data problems in the room, this role was designed for you. Join a team where your code, your models, and your ideas directly shape how Finance leaders make decisions. The impact here is tangible, visible, and yours to own.
As a Vice President — Applied Artificial Intelligence and Data Product Scientist in the Finance Transformation and Analytics team within Asset and Wealth Management Finance, you will be an individual contributor who spends roughly 60% of your time hands-on building and 40% shaping strategy, roadmaps, and best practices. You will own two complementary mandates: first, researching, prototyping, and hardening next-generation AI capabilities so they are production-ready before the current product cycle completes; and second, diagnosing and fixing the structural data problems that undermine both AI and traditional reporting products. You will partner closely with Finance stakeholders, data engineering, and platform teams, but your primary value is building — writing code, training models, designing data architectures, and delivering working products that move measurably faster than the problems they solve.
Job Responsibilities
- Lead research and rapid prototyping of emerging AI capabilities — including agentic workflows, multi-agent orchestration, retrieval-augmented generation, and advanced forecasting — converting validated proofs of concept into production-grade products on a defined timeline
- Maintain a rolling 6–12 month innovation pipeline by identifying, evaluating, and experimenting with new models, frameworks, and techniques so the team always has a next release in development
- Architect, build, and scale large language model agents and AI-driven solutions for Finance workflows, integrating with APIs, microservices, reporting tools, and cloud platforms
- Design evaluation harnesses, guardrails, and human-in-the-loop review processes for every AI product from prototype through production
- Translate model outputs into user-friendly insights, analytics, and decision-support surfaces for Finance leadership and front office partners
- Audit and remediate data quality and lineage gaps across reporting and analytics products so all downstream consumers work from a single source of truth
- Define and implement a data catalogue strategy by inventorying existing assets, consolidating duplicative pipeline logic, standardizing metric definitions, and establishing clear data contracts
- Architect curated and semantic data layers that are consistent, trusted, and reusable across reporting, dashboards, analytics, and AI products
- Establish data observability practices including monitoring, alerting, and lineage tracking so issues surface before they reach end users
- Define and own a forward-looking AI and data roadmap aligned to Finance priorities and the firm's broader technology direction
- Design experimentation and measurement frameworks to quantify the business impact of AI products and data quality improvements, and communicate roadmap, trade-offs, and results to senior leaders through clear narratives and data-driven recommendations
Required Qualifications, Capabilities, and Skills
- 8 years of experience in data science, AI/ML, analytics, or product engineering, with hands-on delivery of production AI or ML applications
- Hands-on experience with large language models including prompt engineering, retrieval-augmented generation, function and tool calling, embeddings, vector stores, and evaluation frameworks
- Strong Python and SQL fluency with experience building and operating production data and inference pipelines end-to-end
- Practical expertise with cloud data platforms such as AWS, Snowflake, or Databricks, including distributed processing and data modeling
- Demonstrated ability to take AI or generative AI solutions from concept through prototype to scaled production
- Experience diagnosing and fixing data quality, lineage, and governance problems in complex enterprise environments
- Understanding of Finance functions such as financial planning and analysis, management reporting, forecasting, and key performance indicator frameworks within asset management, wealth management, or comparable financial services
- Proficiency in applied statistics and experimentation design with the ability to measure business impact and communicate findings to non-technical audiences
- Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, Finance, or a related quantitative field
Preferred Qualifications, Capabilities, and Skills
- Experience building multi-agent systems, autonomous workflows, or task planners using frameworks such as LangGraph, LangChain, or LlamaIndex
- Hands-on experience building generative AI products with guardrails, evaluation harnesses, and human-in-the-loop review grounded in approved internal sources
- Experience designing and governing semantic layers, metric stores, and data observability frameworks that support both business intelligence and AI consumption
- Track record of designing and operationalizing experimentation programs including A/B testing and causal measurement to quantify product and data initiatives
- Knowledge of model safety, bias mitigation, privacy techniques, and AI governance frameworks in financial services
- Familiarity with modern business intelligence and visualization tools such as Tableau, Power BI, or ThoughtSpot
- Prior experience in a Finance transformation or technology function supporting front office or investment management
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
J.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.
Salary Context
This $147K-$215K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 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 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($181K) sits 15% below the category median. Disclosed range: $147K to $215K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
JPMorganChase AI Hiring
JPMorganChase has 218 open AI roles right now. They're hiring across MLOps Engineer, AI Architect, AI Software Engineer, AI/ML Engineer. Positions span Jersey City, NJ, US, New York, NY, US, Plano, TX. Compensation range: $155K - $325K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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