Senior Associate, AI ML Platform Engineering

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.

Job 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.

Required qualifications, capabilities, and skills


* 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/ve...