The Applied Artificial Intelligence and Machine Learning (Applied AI/ML) team within Infrastructure Platforms is transforming how the firm delivers strategic infrastructure platforms-based solutions—both by applying AI/ML within engineering workflows and by building scalable AI hosting platforms and capabilities for enterprise use.
As an Applied ML and Generative Lead within J.P.Morgan, you will operate as a hands-on engineering leader responsible for designing, building, and running production-grade ML and Generative AI services, while setting technical direction that scales across multiple workstreams. You will remain close to the code and architecture decisions, establish delivery and engineering standards, and ensure solutions meet enterprise expectations for security, stability, and operational rigor.
The ideal candidate brings a strong foundation in software engineering and AI/ML, along with proven experience leading the development and production operation of AI-enabled systems in secure, enterprise environments.
In this role, you will collaborate closely with Infrastructure Platforms AI teams to address priority use cases, design and build services, and promote best practices for scalable, resilient, and secure AI adoption. You will also mentor engineers, contribute to firmwide standards and thought leadership, and help ensure the organization stays at the forefront of AI engineering advancements.
Job Responsibilities
- Provide hands-on technical leadership by designing, developing, and deploying ML/LLM/GenAI solutions from concept through production, maintaining ownership for reliability and operability once deployed
- Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases.
- Develop secure, testable services and libraries that integrate LLMs, tool use, RAG, and agentic workflows.
- Build end-to-end RAG/Agentic RAG pipelines: chunking and indexing, retrieval tuning, re-ranking, grounding checks.
- Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation.
- Mentor and uplift junior engineers through design reviews, code reviews, pairing, and coaching, raising engineering quality and delivery discipline across the team.
- Implement monitoring mechanisms to track AI solution performance in real-time to ensure reliability and compliance.
- Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences.
- Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
Required qualifications, capabilities, and skills
- Proven delivery of LLM-enabled applications using agentic patterns, including tool use, orchestration, guardrails, and structured outputs.
- Hands-on experience building and operating MCP integrations reliably in production.
- Hands-on experience on data-driven software/systems engineering experience delivering production services in secure, regulated environments.
- Expertise in Python engineering skills, including production-grade design, testing, debugging, and performance tuning/optimization.
- Advanced prompt engineering capabilities, including system prompts, few-shot prompting, tool/function calling, and schema-constrained outputs (e.g., JSON Schema).
- Understanding of agentic AI system layers and concepts, such as context management, harness design, and loop engineering.
- Experience building conversational AI solutions, including RAG, Agentic and Graph RAG techniques
- Experience building and scaling AI/ML workloads using distributed training/serving frameworks (e.g., Ray) and GPU acceleration (e.g., CUDA) environments.
- Proficiency with modern AI system architectures and patterns, including RAG, agentic RAG, and multi-agent systems.
- Familiarity with LLM evaluation methodologies across quality, safety, and reliability, including guardrails, content filtering, and Responsible AI practices.
- Proficiency in GenAI/agentic AI engineering practices, including data sensitivity, secure handling of inputs/outputs, and adherence to resiliency and security requirements
- Demonstrated success driving adoption of enterprise-approved AI-assisted engineering tools (coding, review, testing, troubleshooting
Preferred qualifications, capabilities, and skills
- Financial Services industry experience
- Understanding of Finops for LLMs
- Good to have Java programming experience
About UsJ.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.
Skills Required
- Proven delivery of LLM-enabled applications using agentic patterns, tool use, orchestration, guardrails, and structured outputs
- Hands-on experience building and operating MCP integrations in production
- Data-driven software or systems engineering experience delivering production services in secure, regulated environments
- Advanced Python engineering skills, including production design, testing, debugging, and performance optimization
- Advanced prompt engineering skills, including system prompts, few-shot prompting, tool or function calling, and schema-constrained outputs
- Understanding of agentic AI system layers, context management, harness design, and loop engineering
- Experience building conversational AI solutions using RAG, Agentic RAG, and Graph RAG
- Experience scaling AI/ML workloads with distributed training or serving frameworks such as Ray and GPU acceleration environments such as CUDA
- Proficiency with RAG, agentic RAG, and multi-agent system architectures
- Familiarity with LLM evaluation across quality, safety, and reliability, including guardrails, content filtering, and Responsible AI practices
- Proficiency in secure GenAI and agentic AI engineering, including data sensitivity, secure input and output handling, resiliency, and security requirements
- Success driving adoption of enterprise-approved AI-assisted engineering tools for coding, review, testing, and troubleshooting
- Financial Services industry experience
- Understanding of FinOps for LLMs
- Java programming experience
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
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Healthcare Strength — Medical, dental, vision, and mental-health coverage are broad, with wellness incentives, on-site or virtual care, and an EAP offering coaching and counseling. Plan materials emphasize accessible options, including multiple medical choices and tools to manage costs.
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Parental & Family Support — Paid parental leave extends up to 16 weeks for all parents, supplemented by paid Critical Caregiver Leave. Family resources include backup childcare via Bright Horizons, lactation support and milk-shipping, family-building assistance, and even a free five-month SNOO rental for newborns.
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Retirement Support — Retirement programs include a 401(k) with an annual company match and automatic pay credits for most employees, with a legacy pension available to earlier hires. An Employee Stock Purchase Plan at a 5% discount further supports long-term savings.
JPMorganChase Insights
What We Do
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
Why Work With Us
Our technologists work on a diverse range of solutions that include strategic technology initiatives, big data, mobile, electronic payments, machine learning, cybersecurity, enterprise cloud development, and other state-of-the-art technologies.
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