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AI/ML Associate Engineer
mid · Technology / Software Development
Join us to shape the future of enterprise AI, where your expertise will help create impactful solutions using cutting-edge technologies. You’ll have the opportunity to work hands-on with LLMs, agentic workflows, and advanced AI controls, collaborating with talented teams across the organization. We value your creativity, technical skills, and passion for building secure, production-ready systems. At JPMorganChase, you’ll find a supportive environment that encourages growth, learning, and meaningful contributions. Discover how you can make a difference and advance your career with us.
As an AI/ML Engineer — Associate Engineer in our AI Workflow Engineering team, you will design, build, and integrate modern AI solutions that power enterprise innovation. You will work across AI workflows, backend services, and production controls, helping us deliver secure, governed, and reliable AI systems. You’ll collaborate with product managers, engineers, and business stakeholders to create impactful solutions. Your role will focus on transforming prototypes into scalable, production-ready applications while fostering a culture of excellence and inclusivity.
Job Responsibilities:
- Design, develop, test, and maintain production-quality AI/ML and GenAI solutions
- Build and integrate LLM-powered workflows, including RAG pipelines, agentic workflows, and workflow orchestration
- Develop backend services, APIs, and integrations to automate AI workflows
- Implement RAG solutions using embeddings, vector stores, hybrid search, and grounded response generation
- Build agentic AI workflows with planning, tool usage, state management, and human-in-the-loop controls
- Implement prompt management, versioning, evaluation harnesses, and quality measurement for LLM-based systems
- Apply AI safety controls such as guardrails, hallucination mitigation, input/output validation, and access control
- Support MLOps / LLMOps practices including CI/CD, automated testing, deployment, monitoring, and lifecycle management
- Instrument AI systems for quality, latency, cost, hallucination risk, tool-call failures, and user feedback
- Collaborate with product managers, engineers, platform teams, and business stakeholders to deliver secure AI workflow solutions
Required Qualifications, Capabilities, and Skills:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or equivalent practical experience
- Hands-on programming experience in Python, Java, C#, or TypeScript
- Strong software engineering fundamentals: data structures, APIs, design patterns, testing, version control, CI/CD, and production support
- Experience building or integrating GenAI / LLM-based applications
- Practical understanding of RAG architecture, including embeddings, chunking, vector search, retrieval evaluation, and grounded response generation
- Exposure to agentic AI patterns, including tool calling, function calling, workflow orchestration, multi-step reasoning, human approval flows, and state management
- Experience building APIs and backend services using REST/gRPC or similar patterns
- Experience with containerization and deployment practices such as Docker and Kubernetes
- Understanding of MLOps / LLMOps, including model/prompt versioning, evaluation, monitoring, release management, and rollback
- Ability to write unit tests, integration tests, and automated validation for AI-enabled systems
- Understanding of AI safety and control patterns, including guardrails, hallucination mitigation, prompt injection risks, and access controls
Preferred Qualifications, Capabilities, and Skills:
- Experience building production-grade agentic AI workflows or AI automation platforms
- Experience with MCP, tool registries, function schemas, or enterprise tool integration patterns
- Experience with vector databases, hybrid search, reranking, knowledge retrieval, or document intelligence systems
- Experience building evaluation frameworks for LLM/RAG systems, including golden datasets, LLM-as-judge, retrieval metrics, and regression testing
- Experience with cloud platforms such as Azure, AWS, or GCP
- Experience with observability tools for tracing, logging, cost tracking, latency monitoring, and quality dashboards
- Experience with MLflow, Kubeflow, LangChain, LlamaIndex, Semantic Kernel, or similar AI orchestration frameworks
- Experience integrating enterprise productivity platforms such as Microsoft 365 Copilot, Copilot Agents, Microsoft Graph, Teams, Outlook, or Power Platform
- Experience working in regulated enterprise environments with security, privacy, audit, and governance requirements