AI Engineer - Lead Software Engineer

JPMorganChase•Palo Alto, CA

About The Position

As a Lead Software Engineer in Enterprise Technology at JPMorganChase, you will lead the architecture and hands-on implementation of scalable large language model systems and agentic AI platforms for enterprise use cases using LLM Suite. You will design cloud-native solutions, establish evaluation and observability standards, and drive technical decisions across teams to improve reliability, cost, and developer velocity. LLM Suite is JPMorganChase’s internally built AI tool, used by more than 250,000 employees for individual productivity and larger-scale business solutions. The role builds and scales production AI platforms that turn large language model capabilities into reliable, secure, and measurable business outcomes. You will partner with product and engineering teams to design architectures, deliver reusable capabilities, and improve quality through strong engineering practices and technical leadership.

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Experience architecting and shipping production large language model applications, including agentic workflows and tool integration patterns.
Strong software engineering fundamentals and ability to deliver cloud-native services using containers and serverless designs on AWS.
Proficiency designing distributed systems with asynchronous workflows, durable messaging, and scalable data access patterns.
Experience building retrieval-augmented generation solutions, including embeddings, semantic search, and grounding, and managing prompt lifecycle/versioning.
Demonstrated ability to implement evaluation and monitoring approaches for model quality, reliability, and safe behavior over time.
Strong API design skills, including secure integration patterns and reusable platform capability development.
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 in the work environment, with ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective use.

Nice To Haves

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.

Responsibilities

Lead architecture and hands-on delivery of scalable, reliable agentic AI platforms for enterprise workflows.
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, 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 connecting AI capabilities to enterprise platforms, tools, and business processes.
Establish evaluation, experimentation, regression testing, and observability frameworks to improve quality and agent behavior continuously.
Define engineering standards for reliability, security, and safe AI operation throughout the platform lifecycle.
Mentor senior engineers and influence engineering direction through code reviews, architecture forums, and cross-team technical leadership.
Use enterprise-authorized AI coding-assistance tools to improve code quality, delivery speed, and productivity; validate outputs through peer review, automated testing, and secure coding standards, and contribute reusable learnings and patterns.
Apply knowledge of software development lifecycle tools, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.