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. You 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
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- 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.
Required qualifications, capabilities and skills- Formal training or certification on software engineering concepts and 8+ years applied experience
- Experience architecting and shipping production large language model applications, including agentic workflows and tool integration patterns
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Familiarity with agentic workflows and frameworks (e.g., LangChain, LangGraph, Autogen, CrewAI and A2A etc). Experience building retrieval-augmented generation solutions (embeddings, semantic search, grounding) using Vector databases and managing prompt lifecycle/versioning
- Strong software engineering fundamentals with ability to deliver cloud-native services using containers and serverless designs on AWS. Advanced python programming skills with experience writing production quality code
- Build systems using frontier models from OpenAI, Anthropic or others leveraging platforms such as AWS Bedrock / Google Vertex AI or other similar platforms
- Proven technical leadership skills, including mentoring, driving architecture decisions, and influencing cross-functional stakeholders
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities and skills- Experience building standardized evaluation harnesses, automated regression suites, and experimentation platforms for large language model systems
- Hands-on experience with Kubernetes-based deployment patterns and operational excellence practices for high-availability services
- Experience applying privacy, data minimization, and safe AI guardrail patterns in regulated or high-risk environments
- Familiarity with context-efficiency optimization techniques and cost governance for large language model workloads. Experience building reusable developer platforms, reference architectures, and technical standards across multiple teams
