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Lead Machine Learning Engineer - Generative AI and Agent Platforms
lead · Technology / Software Development
You’ll build and ship agents that real businesses depend on, not demos. Platform already runs a federated portfolio of production agents — forecasting, anomaly detection, log analysis, with sales fulfillment and voice-of-client close behind — and you’ll add to it. You’ll work across Corporate & Investment Bank sub-lines of business and Payments, using Platform’s runtime, retrieval, and memory primitives plus platforms such as Databricks and the GenAI Gateway, and apply MLOps for automation, continuous delivery, and compliance with AI/ML control expectations. Platform is the firm’s agent runtime: it gives agents secure execution, agent-to-agent (A2A) communication, MCP-based tool access, a managed memory layer, and permission-aware, auditable operation in a regulated environment. Your job is to turn that platform into shipped agents.
As an Applied AI and Machine Learning Lead at JPMorganChase within Payments Technology, Data Analytics, Regulatory & Compliance (Data Analytics) in the Commercial & Investment Bank, you will build and operate unique Agentic operation system and runtime that allows engineering teams build and safely, reliably run their AI agents. You will also build own AI Agents that solve high-impact business problems with measurable outcomes. You will own agent solutions end-to-end, combining strong engineering discipline with rigorous evaluation, safety, and operational excellence. You will partner with product, engineering, and business stakeholders to deliver reliable, auditable AI capabilities at enterprise scale.
Job responsibilities
- Design and deliver production AI agents across a federated portfolio, owning solutions from prototype through launch and operational support
- Engineer retrieval systems that perform reliably in production, including hybrid retrieval-augmented generation patterns (vector search plus graph-based retrieval where appropriate) with robust chunking, ranking, and grounding approaches
- Implement agent memory patterns, including episodic and semantic memory with recall, summarization, and decay policies aligned to use-case needs
- Build entitlement-aware and tenant-aware context assembly so agents reason only over permitted data, supporting traceability and auditability
- Orchestrate multi-agent workflows and integrate external tools and data sources through secure connectors and standardized tool interfaces
- Develop evaluation frameworks, including task-level and end-to-end evaluations, regression suites, automated scoring, and release gates for quality and safety
- Deploy and operate agent services on public cloud platforms (Amazon Web Services and/or Microsoft Azure), applying strong software development lifecycle, security, resiliency, and observability practices
- Optimize runtime performance and reliability by instrumenting tracing, monitoring, and incident-response playbooks for agent services
- Partner with product and business leaders to translate use cases into shipped capabilities, define success metrics, and drive continuous improvement based on production feedback
Required qualifications, capabilities and skills
- Formal training or certification on applied AI and machine learning concepts and 5+ years applied experience
- Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or a related field, or equivalent practical experience
- Minimum 7 years of software development experience, including at least 4 years delivering artificial intelligence or machine learning solutions
- Hands-on experience building large language model–powered or agentic applications in production, including tracing, evaluations, and safety guardrails
- Strong programming skills in Python, including strong fundamentals in data structures, algorithms, and applied statistics
- Practical experience with retrieval-augmented generation, including embedding strategies, retrieval quality measurement, and use of vector databases
- Proficiency operating production workloads in at least one of the following: Amazon Web Services, Microsoft Azure, or Kubernetes
- Experience designing data models and building systems using both SQL and NoSQL technologies for real-time or near-real-time use cases
- Strong communication skills and the ability to partner effectively with senior technical and business stakeholders
Preferred qualifications, capabilities and skills
- Experience with agent frameworks and multi-agent orchestration patterns, including agent-to-agent coordination and Model Context Protocol integrations
- Experience with knowledge graphs and graph databases to improve retrieval quality, explainability, and audit readiness
- Understanding of model optimization techniques, including fine-tuning approaches and efficient inference for smaller models
- Experience developing user-facing applications using modern JavaScript or TypeScript frameworks for agent user interfaces
- Experience delivering AI solutions in financial services or payments environments with high reliability and control expectations
- Familiarity with Go or Rust for performance-sensitive services
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