Applied AI ML Lead – Machine Learning Engineer – Agentic Commerce

Overview

In this role you will design, productionize, and operate LLM-powered agents within JPMorganChase’s Digital & Platform Services / Data Analytics team. You will apply MLOps to automate, ship, and govern secure AI solutions at scale on the NEO platform. You’ll collaborate with product, data science, and engineering teams to expand a portfolio of production AI agents for CIB and Payments. This is a hands-on, impact-focused role in a regulated environment, offering growth and exposure to cutting-edge platforms.

Pay / Benefits

• career growth
• exposure to cutting-edge platforms
• regulated, secure environment
• equal opportunity employer

Responsibilities

• Design and ship production agents on NEO from prototype to production
• Build robust retrieval systems (Graph RAG, vector search, grounding)
• Design agent memory (episodic/semantic) and decay policies
• Manage secure context (entitlement, lineage, tenant awareness) for agent reasoning
• Compose multi-agent workflows (A2A) and integrate data/tools via MCP servers (Bitbucket, Confluence, Databricks, Kubernetes, Snowflake, Splunk)
• Build/run agent evaluations, regression suites, LLM-as-judge, quality/safety gates
• Deploy and operate on public cloud (AWS/Azure) with strong SDLC, security, resiliency, observability
• Collaborate with product/business teams to ship supported agents
• Build ML model training pipelines and productionize with MLOps
• Develop batch and online inference for ML models

Key requirements

• MS in Computer Science, Statistics, Mathematics, Machine Learning, or related field (or equivalent experience)
• Hands-on experience building production-grade LLM-powered or agentic applications
• Strong Python programming skills and solid knowledge of data structures, algorithms, ML, information retrieval, and statistics
• Knowledge of Kubernetes (AWS EKS)
• Experience with Databricks and SageMaker
• Experience with MLFlow
• Practical RAG experience (retrieval quality, embeddings, vector stores); Graph RAG is a plus
• Expert knowledge of at least one of: AWS, Azure, Kubernetes
• Knowledge of data management and data model design; real-time processing with SQL (Postgres) and NoSQL (OpenSearch, Redis)
• Excellent communication skills with senior stakeholders

• strong communication
• team collaboration
• problem-solving
• LLM-powered or agentic applications
• MLOps
• Graph RAG and embeddings

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