Interested in this AI Software Engineer role at JPMorganChase?
Apply Now →Skills & Technologies
About This Role
JOB DESCRIPTION
We have an exciting and rewarding opportunity for you to take your data engineering career to the next level.
As a Lead Software Engineer \- Databricks/PySpark/AI at JPMorganChase within the Corporate Sector\-Global Finance team, you will serve as a senior hands\-on developer and technical leader within an agile team, responsible for building, delivering, and optimizing cutting\-edge data products that power agentic AI systems — autonomous AI agents capable of planning, reasoning, and executing multi\-step tasks. In this role, you will write production\-quality code daily, drive implementation of essential technology solutions including data infrastructure, tool integrations, and retrieval systems that enable AI agents to access, interpret, and act on enterprise data in support of the firm's business goals. You will be expected to mentor junior engineers, collaborate with cross\-functional stakeholders, and champion engineering excellence through hands\-on delivery.
Job Responsibilities
- Building and optimizing data pipelines and workflows that serve as the backbone for agentic AI systems, ensuring agents have reliable, real\-time access to high\-quality, structured and unstructured data
- Developing data retrieval and indexing layers that enable AI agents to autonomously search, query, and synthesize information across multiple data sources
- Building and maintaining tool\-use infrastructure — APIs, data services, and function endpoints — that AI agents invoke to execute tasks, retrieve data, and interact with enterprise systems
- Implementing and enforcing best practices for data management, ensuring data quality, security, and compliance, including governance of data consumed and generated by autonomous AI agents
- Hands\-on development of secure, high\-quality production code following AWS best practices, and deploying efficiently using CI/CD pipelines;
Building orchestration and state management layers that support multi\-step agent workflows, including memory, context persistence, and task chaining
- Writing and reviewing code daily, conducting thorough code reviews, and raising the technical bar across the team;
Mentoring and guiding junior and mid\-level engineers through pairing, code reviews, and technical coaching
- Collaborating with product owners, data scientists, and business stakeholders to translate business requirements into working, production\-ready agentic AI solutions;
Evaluating and adopting emerging agentic AI frameworks, tools, and data engineering practices to continuously improve the team's development capabilities
Required Qualifications, Capabilities, and Skills
- Formal training or certification on software engineering concepts and 5\+ years applied experience
- Expert\-level programming skills in Python/PySpark with a strong portfolio of production\-grade code
- Extensive hands\-on experience with Databricks and the AWS cloud ecosystem, including AWS Glue, S3, SQS/SNS, Lambda
- Deep expertise with Spark and SQL
- Strong hands\-on experience with Lakehouse/Delta Lake architecture, application development, testing, and ensuring operational stability; Snowflake, Terraform and LLMs; Data Observability, Data Quality, Query Optimization \& Cost Optimization
- In\-depth knowledge of Big Data and data warehousing concepts at enterprise scale
- Extensive experience with CI/CD processes and automated testing frameworks
- Solid understanding of agile methodologies, including DevOps practices, application resiliency, and security measures
- Understanding of agentic AI concepts — how autonomous AI agents plan, reason, use tools, and execute multi\-step workflows — and the data infrastructure required to support them
- Experience building APIs, data services, and retrieval systems that serve as the connective tissue between AI agents and enterprise data
- Demonstrated ability to lead by example through code, mentor engineers, and drive delivery across the team
Preferred Qualifications, Capabilities, and Skills
- Experience with agentic AI frameworks (e.g., LangGraph, AutoGen, CrewAI, OpenAI Assistants API) and understanding of how data engineering underpins agent orchestration
- Familiarity with tool\-use and function\-calling patterns for LLM\-based agents, including building and exposing APIs and data endpoints that agents can invoke autonomously
- Experience with vector databases (e.g., Pinecone, FAISS, Chroma) and embedding workflows for powering agent memory, semantic search, and retrieval\-augmented generation (RAG)
- Exposure to agent memory and state management patterns — short\-term context windows, long\-term persistent memory stores, and conversation/task history management
- Familiarity with guardrails and safety frameworks for autonomous AI systems, including input/output validation, action approval workflows, and human\-in\-the\-loop controls
- Understanding of observability and monitoring for agentic systems — tracing agent decision paths, logging tool invocations, and debugging multi\-step autonomous workflows
- Understanding of responsible AI principles, particularly around autonomous decision\-making, data provenance, and auditability of agent actions
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.
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 3,823 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 $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
JPMorganChase AI Hiring
JPMorganChase has 76 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, MLOps Engineer. Positions span Jersey City, NJ, US, Chicago, IL, US, Columbus, OH, US. Compensation range: $131K - $325K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.