Overview

Join JPMorgan Chase as a Senior Data Engineer with machine learning and AI expertise. You'll work on building full-stack ML systems, integrating APIs, implementing RAG capabilities, and operationalizing LLM solutions.

Responsibilities

• Design and build ML systems end-to-end: problem framing, data prep, feature engineering, model training, evaluation, deployment, monitoring, and iteration
• Develop agentic AI solutions: LLM agents that plan, call tools/APIs, run multi-step workflows, and apply guardrails/fail-safes
• Implement RAG capabilities: retrieval strategy, chunking, embeddings, indexing, and re-ranking to ground agent responses in knowledge
• Build full-stack product experiences for ML/agent systems: backend services plus frontend UIs for configuration, human-in-the-loop review, observability, and workflow execution
• Develop and integrate APIs/services: design and implement RESTful (and/or event-driven) integrations to serve models, agents, features, and data products
• Build scalable data pipelines with Apache Spark (PySpark/Scala) for batch processing and feature generation
• Use SQL extensively for exploration, transformations, validation checks, and query performance tuning on large datasets
• Operationalize and evaluate ML/LLM systems with MLOps: CI/CD, model registry, experiment tracking, reproducible training, automated evaluation/regression tests, and quality frameworks

Qualifications

• 3+ years delivering production ML solutions, owning the end-to-end lifecycle from problem framing through deployment and iteration
• Experience in React, octagon framework for UI development
• Experience in building, integrating APIs for Experience services
• Strong ML fundamentals including supervised/unsupervised learning, feature engineering, model evaluation, bias/variance tradeoffs, and error analysis
• Hands-on agentic AI / LLM application development, including tool/function calling, planning, and memory patterns
• Experience building RAG pipelines covering retrieval strategies, chunking, embeddings, and re-ranking approaches
• Implemented LLM guardrails such as policy checks, refusal handling, PII filtering, and deterministic fallback behaviors
• Strong Python programming skills for building ML/LLM systems and supporting tooling
• Full-stack engineering experience building/operating backend APIs/services (REST and/or event-driven) and modern web frontends (React/Angular/Vue) for HITL workflows, configuration, and monitoring/analytics
• Advanced data engineering/query skills including expert SQL (joins, window functions, CTEs, optimization), strong Apache Spark/PySpark (DataFrames, Spark SQL, tuning/partitioning)
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment

Benefits

• Dynamic, high-performing team
• Opportunity to leverage agentic AI capabilities in delivering scalable solutions
• Work with cutting-edge technologies and methodologies across full-stack development, data engineering, and ML enablement