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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
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As the Corporate Technology - AI ML Lead, you will join the Corporate Technology team to lead the engineering build of agentic AI and LLM-powered products for advisors and clients across international markets. You will own the end-to-end delivery of priority use cases, from initial problem framing and business case development through to deployed, monitored production services. Your daily responsibilities include setting engineering quality bars via code reviews and technical design, establishing Responsible AI controls like guardrails and evaluation frameworks, and partnering with stakeholders to shape funded workstreams. You will utilize Python, CI/CD, containerization, and cloud-native deployment patterns while leveraging tools like Claude Code and GitHub Copilot. Your expertise must include RAG, fine-tuning, prompt engineering, and agentic frameworks to solve complex technical problems within the financial services domain.
Deliver end-to-end AI/ML use cases from initial problem framing and business case through to production.
Lead the engineering build of agentic AI and LLM-powered products for international markets.
Set engineering quality standards through code reviews, technical design, and peer mentoring.
Establish and operate Responsible AI controls including guardrails, evaluation frameworks, and model risk controls.
Partner with business stakeholders to identify and shape new AI/ML opportunities into funded workstreams.
Represent the team in firm-wide governance forums to ensure regulatory and data-privacy compliance.
What we're looking for
Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field (or equivalent applied experience).
Formal training or certification on AI/ML engineering concepts and 5+ years of applied experience.
Advanced proficiency in Python and modern software engineering practices including testing, design patterns, code review, and version control.
Fluency with AI coding tools as a core part of day-to-day software development.
Hands-on experience building, evaluating, and deploying machine learning models, LLMs, prompt engineering, RAG, fine-tuning, and agentic frameworks.
Practical experience with CI/CD, containerization, and cloud-native deployment patterns.
Demonstrated experience delivering system design, application development, testing, and operational stability for ML or data-intensive systems.
Industry-recognized cloud or GenAI certification (preferred); experience in financial services technology (preferred); experience with Databricks, Kubernetes, or similar platforms (preferred).
LLM
NLP
Python
Java
LangChain
LangGraph
AutoGen
RAG
ML Ops
Terraform
CloudFormation
Jenkins
GitLab CI/CD
API
Big Data
Search
Software Development Life Cycle
Machine Learning
Machine Learning
Deep Learning
Large Language Models
Generative AI
Python
PyTorch
TensorFlow
RAG
Prompt Engineering
Multi-agent Orchestration
AWS
SageMaker
EKS
Distributed Training
Reinforcement Learning
Meta Learning
Search/Ranking
Recommender Systems
Graph Techniques