Lead Software Engineer - AI Application

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within Corporate Technology, you will lead the architecture and hands-on implementation of scalable GenAI Applications and agentic AI platforms for Finance use cases leveraging Firmwide AI tools & platforms. You will design cloud-native solutions, establish evaluation and observability standards, and drive technical decisions across teams to improve reliability, cost, and developer velocity. The candidate will design cloud-native AWS services and reusable platform capabilities (agents, retrieval/RAG, guardrails, tool orchestration, APIs), while establishing strong evaluation, observability, reliability, security, and cost controls. Ideal candidates have extensive experience, advanced Python, proven delivery of LLM/agentic systems, and technical leadership skills to mentor engineers and drive cross-team architecture standards in a regulated enterprise environment.

Job responsibilities

• Lead the architecture and hands-on delivery of scalable, reliable agentic AI platforms for enterprise workflows

• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.

• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

• Design and build production-grade AI systems including agents, skills, memory patterns, guardrails, and tool-use orchestration

• Architect retrieval and context-engineering approaches including embeddings, semantic search, grounding, summarization, and prompt/version management

• Engineer cloud-native AI services on AWS using containers and serverless patterns, event-driven messaging, and distributed data stores

• Optimize platform performance across latency, throughput, scalability, caching, context efficiency, and cost controls

• Build well-governed APIs and integrations that connect AI capabilities to enterprise platforms, tools, and business processes

• Establish evaluation, experimentation, regression testing, and observability frameworks to continuously improve quality and agent behavior

• Mentor senior engineers and influence engineering direction through code reviews, architecture forums, and cross-team technical leadership