Senior Associate, AI ML Platform Engineering

JPMorgan Chase & Co.•Jersey City, NJ

About The Position

Join the AI/ML Data Platforms team to build products that drives MLOps, automated governance and ML data development to accelerate a diverse and broad portfolio of AI/ML projects in one of the largest financial services firms in the world. We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As an Applied AI/ML Senior Associate at JPMorgan Chase within the Corporate AI/ML Data Platforms team, you will build a set of products that covers Agentic Ops, MLOps, ModelOps, ML data development (e.g. processing, data annotation) for firm wide ML practitioners.

Requirements

Graduation or master’s degree (or equivalent practical experience) in Computer Science, Data Science, Machine Learning, or related field.
Hands-on experience building applied AI/ML or GenAI solutions (e.g., RAG, classification, extraction, ranking, summarization, copilots).
Familiarity with MCP (Model Context Protocol), Agent Skills and architectures that connect models to tools/data through standardized interfaces.
Familiarity with LLM application patterns: embeddings/vector search, prompt orchestration, tool calling/function calling, safety/guardrails, evaluation.
Strong software engineering experience delivering production systems; ability to design maintainable architectures and write clean, testable code.
Proficiency in Java and/or Python and experience building APIs/services and integrating with data sources and downstream systems.
Experience deploying solutions on AWS and cloud-native environments; understanding of security fundamentals and operational excellence.
Experience with modern engineering practices: CI/CD, code reviews, unit testing (e.g., pytest/JUnit), and deployment automation.
Experience with containers and orchestration (e.g., Docker, Kubernetes/EKS/ECS) and production monitoring practices.
Ability to communicate complex ideas effectively
Passion for growing your skills, tackling interesting work and challenging problems

Nice To Haves

Experience building agentic AI systems (multi-step workflows, tool routing, planning, memory patterns, supervision/fallback strategies).
Experience with AWS Bedrock and/or SageMaker (or equivalent managed ML/GenAI platforms) and deployment patterns for scalable inference.
Experience with evaluation frameworks and approaches (golden datasets, LLM-as-judge, human-in-the-loop review, red teaming).
Experience fine-tuning models (e.g., LoRA/QLoRA/DoRA) and/or working with SLMs, embeddings, and retrieval systems.
Experience with developer productivity tooling such as GitHub Copilot and Claude Code, paired with strong SDLC controls.
Knowledge of the financial services industry and operating in regulated environments (auditability, controls, data handling).
Exposure to distributed compute/training concepts (e.g., DDP, sharding) and performance/cost optimization.

Responsibilities

Works on several new systems including model repository/registry, feature registry, automatic model promotion policy engine, model & GenAI governance tools, data annotation, data preparation and lineage to help accelerate AI/ML in JPMC with the best user experience and sound governance.
Design, develop, and deploy GenAI and Agentic AI solutions that improve automation, decision-making, and user experience across business workflows.
Build LLM/SLM - powered applications including RAG-based systems, summarization/extraction pipelines, chat/coplay experiences, and tool-using agents.
Engineer production-grade services using Java and/or Python (GraphQL/REST/gRPC APIs, microservices, libraries), following secure coding and reliability best practices.
Develop new products leveraging cloud technologies and micro-services architecture patterns, identify new open-source libraries, using unfamiliar technologies and learning new programming languages to meet technical requirements.
Develop prompt strategies and prompt engineering assets (templates, routing, guardrails), and implement automated evaluation to improve quality over time.
Build and maintain data pipelines and processing workflows required for ML/GenAI use cases using cloud services.
Apply MLOps practices across the lifecycle: experimentation, versioning, CI/CD, deployment, monitoring, and maintenance for models/prompts/agents.
Implement robust testing (unit/integration), performance benchmarking (latency/cost), and observability (logging/metrics/tracing) for AI services.
Collaborate with cross-functional stakeholders to define requirements, success metrics, and rollout plans; communicate complex topics clearly to technical and non-technical audiences.