Lead ML Engineer, Agent Platform & Secure Runtime

JPMorgan Chase

Jersey City (NJ)

On-site

USD 152,000 - 215,000

Full time

14 days+

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Job summary

JPMorgan Chase in Jersey City, NJ is seeking a Lead Software Engineer - ML Engineer for the Agent Platform within the Commercial and Investment Banking - Data Analytics Payments Team. You will lead hands-on engineering of major runtime components, secure execution, agent-to-agent communication, memory, retrieval, and evaluation in a secure, scalable way.

You will mentor engineers, drive AI-assisted practices, ensure secure coding standards, and collaborate with internal teams and external

Qualifications

Formal training or certification in software engineering concepts with 5+ years of applied experience.
Hands-on experience delivering system design, development, testing, and operational stability.
Advanced in one or more programming languages; strong Python required.
Experience leading use of AI-assisted development tools with validation for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows and secure handling of data/outputs.
Hands-on experience building LLM-powered or agentic systems with tracing and guardrails.
Proficient in all aspects of the Software Development Life Cycle.
Advanced understanding of Agile methodologies including CI/CD, resilience, and security.
Proficient in software applications and technical processes in cloud/AI/ML.
In-depth knowledge of the financial services industry and IT systems.
Cloud-native experience; production Kubernetes expected.

Responsibilities

Executes creative software solutions and technical troubleshooting for novel problems.
Builds and operates major NEO runtime components and integrations.
Develops secure, high-quality production code and assists in code reviews.
Drives adoption of AI-assisted engineering practices to improve quality and delivery.
Applies SDLC toolchains and automation to increase value.
Identifies opportunities to automate recurring issues for stability.
Implements permission-aware, auditable execution for agents.
Leads evaluation sessions with vendors and internal teams on architecture.
Leads communities of practice and mentors engineers.
Contributes to an inclusive, diverse team culture.