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About This Role
JOB DESCRIPTION
Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top\-notch technology products.
As a Senior Lead Software Engineer\- Python Distributed Development and AI Modernization at JPMorganChase within the Consumer and Community Bank\- Wealth Management Technology, you are an integral part of an agile team that works to enhance, build, and deliver trusted market\-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem\-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
In this role you will design, build, and ship the agentic systems that ingest decades of mainframe logic and produce verified, production\-ready modern services. You work directly alongside domain SMEs to turn legacy COBOL, JCL, DB2, and batch schedules into structured specifications — then drive those specs through agent\-accelerated delivery into the target platform. You are a builder first: comfortable architecting multi\-agent orchestration one day and debugging a prompt chain against production edge cases the next. You have deep proficiency extending and operating coding agents (Claude Code, Codex, Copilot), and you bring the engineering rigor to make AI outputs reliable at enterprise scale. You thrive on hard problems, move fast, and care deeply about shipping software that works.
Job responsibilities
- Builds and operates the spec generation pipeline — Implement artifact ingestion (COBOL source, JCL, job schedules, DB2 schemas, SME\-captured knowledge), chunking strategies, and RAG pipelines that produce structured calculation and workflow specifications validated by domain experts.
- Develops agentic workflows for code translation and migration — Design, implement, and iterate on multi\-agent systems that translate legacy logic into target\-state code (Kotlin/JVM). Build orchestration layers, tool\-use patterns, and guardrails that ensure output correctness for financial calculations.
- Builds evaluation and verification infrastructure — Create automated test harnesses that compare migrated calculation outputs against legacy results. Implement parity testing frameworks, regression suites, and confidence scoring to gate production cutover decisions.
- Contributes to the standard calculation runtime — Help build and extend the target platform that migrated calculations deploy into. Ensure the runtime supports deterministic, immutable, auditable execution.
- Partners with domain SMEs — Embed with mainframe subject\-matter experts across Credit, Money Market \& Mutual Funds, Statements \& Tax, and IBOR to validate agent outputs, refine prompt strategies, and close knowledge gaps in specifications.
- Extends ETL and CDC pipelines for agent workflows — Build and integrate event sourcing, CDC (change data capture), and data pipelines that support end\-to\-end migrated workflows, including upstream/downstream dependency mapping.
- Operates AI systems in production — Own LLMOps for the toolchain: deployment, monitoring, cost management, latency optimization, token budget management, and incident response. Ensure reliability and compliance for 24/7 operation.
- Iterates rapidly and ships continuously — Work in tight build\-measure\-learn cycles. Prototype quickly, instrument everything, and make data\-driven decisions about agent architectures, model selection, and prompt strategies.
- Contributes to shared tooling and infrastructure — Build reusable libraries, evaluation harnesses, prompt templates, and orchestration patterns that scale AI capabilities across all four core processing domains.
- Drives adoption and governance of approved AI\-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI\-assisted code review/refactoring, test acceleration, release readiness, incident/root\-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI\-assisted development and automation capabilities, to improve the value realized by automation at scale.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5\+ years applied experience
- Hands\-on experience building LLM\-based applications — agentic architectures, RAG pipelines, prompt engineering, and evaluation frameworks
- Strong software engineering fundamentals: distributed systems, event\-driven architectures, API design, testing practices, and cloud platforms (AWS/EKS/ECS)
- Expert proficiency with AI\-assisted development tools (Claude Code, GitHub Copilot, Cursor) as core daily workflow
- Strong experience with Python development in production environments
- Demonstrated ability to operate and debug complex systems — you own what you ship
- Clear communicator who can articulate technical trade\-offs to both engineers and business stakeholders
- Demonstrated experience leading effective use of enterprise\-authorized AI\-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
- Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field
Preferred qualifications, capabilities, and skills
- Experience with legacy systems, mainframe technologies (COBOL, JCL, DB2\), or large\-scale migration programs
- Familiarity with workflow orchestration (Temporal, Airflow) and event sourcing / CDC patterns and experience building code analysis, translation, or verification tooling
- Experience with Kafka, PostgreSQL, and container orchestration (Kubernetes/EKS)
- Background in financial services — wealth management, brokerage, or capital markets processing
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 Consumer \& Community Banking Group depends on innovators like you to serve consumers, small businesses, municipalities and non\-profits. You'll support the delivery of award winning tools and services that cover everything from personal and small business banking as well as lending, mortgages, credit cards, payments, auto finance and investment advice. This group is also focused on developing and delivering cutting edged mobile applications, digital experiences and next generation banking technology solutions to better serve our clients and customers.
Salary Context
This $171K-$260K range is above the median for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At JPMorganChase, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $171K to $260K.
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 Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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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