Interested in this AI Software Engineer role at JPMorganChase?
Apply Now →Skills & Technologies
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\- Java/Python/ AI Solutions at JPMorganChase within the Asset and Wealth Management Technology 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. 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.
This is a rare opportunity to help shape the future of our Private Bank. With the sponsorship from the CEO and the heads of the business, our goal is to create an Agentic Private Bank \- reimagining the entire process from start to finish, rethinking the operating model including organizational structures and developing AI agents equipped with the latest tools and technologies to fundamentally reshape how we perform this business.
Join our dynamic team of innovators and technologists, where your mission will be to revolutionize how the Bank services and advises clients, deepen client engagements, and drive process transformation. Our culture thrives on experimentation, continuous improvement, and learning. You will work in a collaborative, trusting, and intellectually stimulating environment—one that values diversity of thought and fosters creative solutions that serve the best interests of our global clientele.
Job responsibilities
- Lead the end\-to\-end design, development, and deployment of client\-facing Generative AI and Agentic AI solutions that enhance automation, personalization, and decision\-making for external clients.
- Architect and implement prompt\-based models on Large Language Models (LLMs) for NLP tasks tailored to financial services use cases such as document summarization, intelligent search, conversational interfaces, and advisory support.
- Design and build autonomous agentic AI workflows capable of multi\-step reasoning, tool use, and task execution with appropriate human\-in\-the\-loop guardrails aligned with emerging enterprise patterns such as OpenAI Frontier and Anthropic Claude Cowork, and implement Model Context Protocol (MCP) integrations to enable AI agents to securely connect to and interact with external data sources, APIs, and enterprise tools in real time.
- Build and maintain scalable data pipelines and data processing workflows for both structured and unstructured data, leveraging cloud services to support LLM\-based features and real\-time client interactions and design and develop robust APIs and microservices to integrate AI/LLM models into client\-facing platforms, ensuring seamless, low\-latency experiences.
- Drive 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.
- Apply 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.
- Develop secure, high\-quality, production\-grade code that powers client\-facing applications, ensuring reliability, performance, and compliance with financial industry standards.
- Produce architecture and design artifacts for complex, distributed applications while ensuring design constraints, including latency, throughput, and regulatory requirements, are met and implement observability, monitoring, and feedback loops for agentic AI systems to track agent behavior, detect hallucinations, and ensure reliability in high\-stakes financial applications
- Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse datasets to inform product improvements, monitor model performance, and enhance client outcomes and proactively identify hidden problems and patterns in data and system behavior, using these insights to drive improvements to code quality, system resilience, and client experience.
- Partner closely with product, design, and business stakeholders to translate client needs and business requirements into scalable AI\-driven technical solutions.
- Ensure all client\-facing solutions adhere to strict security, privacy, and regulatory standards applicable to the financial services industry, including data protection, model governance, and responsible AI practices, particularly as regulators increase scrutiny of autonomous AI agents in financial services.
Required qualifications, capabilities, and skills
- Formal training or certification in software engineering concepts with 5\+ years of applied experience
- Strong programming proficiency in Python or Java, with demonstrated experience building AI/ML\-powered applications
- Proven experience designing and developing client\-facing applications with a focus on usability, performance, and reliability at scale
- Hands\-on experience building data pipelines for both structured and unstructured data processing in support of AI/ML workloads
- Experience developing RESTful APIs and microservices and integrating NLP or LLM models into production software applications, and hands\-on experience with cloud platforms (AWS or Azure) for AI/ML model deployment, data processing, and infrastructure management
- 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 building agentic AI systems with multi\-step reasoning, tool orchestration, and autonomous task execution within guardrailed environments
- Familiarity with agentic AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar libraries
- Strong understanding of security best practices, particularly in the context of client\-facing financial applications (e.g., data encryption, access controls, regulatory compliance)
- Solid understanding of the Software Development Life Cycle (SDLC) and agile methodologies including CI/CD, application resiliency, and DevSecOps, and experience working in a large corporate or financial services environment, with familiarity in navigating complex stakeholder landscapes and regulatory frameworks
Preferred qualifications, capabilities, and skills
- Experience with both Java and Python
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 $171K-$260K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $183K across 194 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 3,708 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 $219,250 based on 424 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 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 3,708 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 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 3,708 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.