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About This Role
JOB DESCRIPTION
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer\- Financial Service Data Engineering: Pyspark / Java / BigData / Datalake / AI, at JPMorganChase within the Asset and Wealth Management\- Global Prime Brokerage Team, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.
We look for people who are passionate about solving business problems through innovation, analytics, and an AI‑first engineering mindset—building reusable, governed analytical data products and accelerating delivery of regulatory and CEO‑priority analytics. You will define and enforce an AI\-driven data product lifecycle (semantic alignment, automated lineage and data quality, pipeline/code generation, mesh registration, and self\-service consumption) and will build human\-in\-the\-loop autonomous agents to detect schema drift, propose transformations, reconcile semantics, triage data incidents, and generate governance evidence. You'll be required to apply your depth of knowledge and expertise to all aspects of the analytics development lifecycle, and partner continuously with stakeholders across product, platform, risk, and domain teams. You will lead an AI‑first transformation of data engineering and analytics by productizing the data product lifecycle (semantics, lineage, DQ, governance) and building autonomous agents (human‑in‑the‑loop) that reduce manual toil, improve auditability, and enable self‑service consumption on the strategic data mesh. The role also owns modernization of the strategic data mesh.
Job responsibilities
- Collaborate with business and technology teams to develop AI‑first analytics and data product solutions
- Define and enforce architecture for an AI‑driven data product lifecycle: semantic extraction/alignment, automated lineage and DQ, pipeline code generation, mesh registration, and self‑service consumption
- Build and operate autonomous agents for data engineering that detect schema drift, propose transformations, reconcile semantics, triage data incidents, and maintain governance evidence under human‑in‑the‑loop controls
- Design analytics platforms capable of running reporting and other analytics; explore innovative ideas by building real‑time and batch analytics solutions
- Establish appropriate monitoring and alerting of solution events related to performance, scalability, availability, and reliability
- Provide technical leadership, guidance, and direction to other team members; build prototypes for demonstrations for peer groups, business partners, and senior leaders
- Drive team adoption of enterprise\-authorized AI\-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI\-assisted code review/refactoring, test strategy acceleration, incident/root\-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
- Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise\-authorized AI\-assisted development and automation capabilities, to improve the value realized by automation
- Lead migration and modernization from legacy analytics/reporting stacks to the strategic mesh ecosystem (e.g., Databricks/Iceberg/common services), reducing fragmentation and duplicated data products
- Industrialize entity resolution and parent identification with ML/LLM solutions and standardize analytical product packaging to enable reuse and monetization
- Embed governance, lineage, and DQ by design across critical domains and regulatory reporting, improving auditability and control posture
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5\+ years applied experience
- Proven leadership delivering AI‑first analytics and data engineering at scale, including productized data mesh patterns, semantic layers, and analytical data product lifecycle ownership
- Experience developing data ingestion and integration processes, sourcing data from multiple platforms, and applying data cleansing/transformation rules for analytics\-ready datasets
- Deep hands‑on experience with big data and modern data platforms (e.g., Spark, Databricks, Snowflake, Iceberg) and building robust pipelines and data lake/lakehouse frameworks
- Strong programming capability in Python and PySpark, or Java, with strong CI/CD and containerization practices
- Applied AI expertise in ML pipelines, NLP/LLMs, and agentic frameworks to build autonomous agents for engineering tasks (schema drift detection, semantic reconciliation, incident triage, governance evidence generation) under human‑in‑the‑loop controls
- Governance proficiency across lineage, data quality, and access control with evidence generation aligned to regulatory expectations (e.g., BCBS 239‑class lineage/DQ)
- Comfortable working in an agile and collaborative environment; strong written and verbal communication skills
- Demonstrated experience leading effective use of approved AI\-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices
- Proficient in all aspects of the Software Development Life Cycle
Preferred qualifications, capabilities, and skills
- Python and Java
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.
Our Asset and Wealth Management division is driven by innovators like you who are driven to create technology solutions that make us work more efficiently and help our businesses grow. It's our mission to efficiently take care of our clients' wealth, helping them get, and remain properly invested. Our team of agile technologists thrive in a cloud\-native environment that values continuous learning using a data\-centric approach in developing innovative technology solutions.
Salary Context
This $152K-$215K range is below the median for AI Software Engineer roles in our dataset (median: $185K across 231 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At JPMorganChase, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($183K) sits 16% below the category median. Disclosed range: $152K to $215K.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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