JPMorgan Chase
AI Engineer - Lead Software Engineer
$157K–$215K
Jersey City, NJ +3 · Full-time · Posted Oct 9, 2026
- Location
- Jersey City, NJ
Wilmington, DE
Seattle, WA
Palo Alto, CA - Experience
- 5+ years applied experience.
- Education
- Formal training or certification on software engineering concepts.
About the job
Leads the architecture and implementation of scalable enterprise LLM and agentic AI platforms, including cloud-native services, evaluation, observability, and engineering standards.
What you'll do
- Architect and deliver production agentic AI systems, including retrieval, orchestration, guardrails, and enterprise integrations.
- Build and optimize AWS-based AI services for reliability, performance, security, and cost.
- Establish evaluation and observability practices, set engineering standards, and mentor engineers across teams.
What they ask for
- Formal training or certification in software engineering concepts and 5+ years of applied experience.
- Experience architecting and shipping production LLM applications, including agentic workflows and tool integration.
- Experience building cloud-native AWS services and distributed systems with asynchronous workflows, messaging, and scalable data access.
- Experience with RAG, prompt lifecycle/versioning, model evaluation and monitoring, secure API design, technical leadership, and enterprise-authorized AI-assisted development.
Nice to have
- Experience with evaluation harnesses, automated regression suites, and experimentation platforms; Kubernetes deployment and high-availability operations.
- Experience with privacy and AI guardrails in regulated or high-risk environments, context efficiency and cost governance, or reusable developer platforms and standards.
Pay
USD 156,750.00-$215,000.00 base salary (Jersey City, NJ); USD 156,750.00-$215,000.00 base salary (Seattle, WA); USD 156,750.00-$215,000.00 base salary (Palo Alto, CA)
Tools
LLM Suite, AWS, Kubernetes, embeddings, semantic search
AI focus
Leads enterprise LLM and agentic AI platform development, including RAG, tool orchestration, evaluation, observability, and safe AI operations; uses enterprise-authorized AI-assisted development tools.
Model-written summary · the original posting is authoritative