Lead Machine Learning Engineer, Generative AI and Agent Platforms

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Jersey City, NJ
Posted
59 days ago
Freshness
Confirmed live yesterday

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Salary context

How this pay compares to similar roles

Similar $222k
$175k most similar roles pay here $272k

This listing doesn't post a salary. Most similar roles pay $194,962–$248,721.

Based on 240 similar postings.

Employer

About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Lead Machine Learning Engineer, Generative AI and Agent Platforms

Lead Machine Learning Engineer - Generative AI and Agent Platforms will join the Payments Technology, Data Analytics, Regulatory & Compliance team to build and operate a unique agentic operating system and runtime. This role involves designing and delivering production AI agents across a federated portfolio, including retrieval systems using hybrid RAG patterns, memory models with decay policies, and multi-agent workflows. The candidate will develop evaluation frameworks, manage secure tool integrations, and deploy services on Amazon Web Services or Microsoft Azure while ensuring compliance in regulated environments. Key technical requirements include proficiency in Python, SQL, NoSQL, and experience with vector databases, knowledge graphs, and MLOps for continuous delivery. The role addresses complex business problems within Corporate & Investment Bank sub-lines and Payments by creating reliable, auditable AI capabilities using Databricks and the GenAI Gateway.

What you'll do

  • Design and deliver production AI agents across a federated portfolio from prototype through launch.
  • Engineer reliable retrieval systems using hybrid RAG patterns, including vector search and graph-based retrieval.
  • Implement agent memory patterns such as episodic and semantic memory with specific recall and decay policies.
  • Build entitlement-aware context assembly to ensure agents only reason over permitted data for auditability.
  • Orchestrate multi-agent workflows and integrate external tools through secure connectors and standardized interfaces.
  • Develop evaluation frameworks including regression suites, automated scoring, and release gates for safety.
  • Deploy and operate agent services on public cloud platforms using robust SDLC and observability practices.
  • Optimize runtime performance by instrumenting tracing, monitoring, and incident-response playbooks.

What we're looking for

  • Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, or a related field, or equivalent practical experience.
  • Formal training or certification on applied AI and machine learning concepts and 5+ years of applied 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 with tracing, evaluations, and safety guardrails.
  • Strong programming skills in Python, including fundamentals in data structures, algorithms, and applied statistics.
  • Practical experience with retrieval-augmented generation, embedding strategies, retrieval quality measurement, and vector databases.
  • Proficiency operating production workloads in Amazon Web Services, Microsoft Azure, or Kubernetes.
  • Experience designing data models and building systems using both SQL and NoSQL technologies for real-time use cases.
  • Experience with agent frameworks, multi-agent orchestration, knowledge graphs, or model optimization techniques (preferred).
  • Experience delivering AI solutions in financial services or familiarity with Go or Rust (preferred).

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