AI Engineer - Lead Software Engineer

JPMorgan Chase & Co.•Jersey City, NJ

5d•$152,000 - $215,000

About The Position

As a Lead Software Engineer at JPMorganChase within Enterprise Technology, you will lead the architecture and hands-on implementation of scalable large language model systems and agentic AI platforms for enterprise use cases leveraging 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 premier internally built AI tool leveraged by +250k employees for everything from individual productivity to larger scale, business solutions. Build and scale production AI platforms that turn large language model capabilities into reliable, secure, and measurable business outcomes. You will partner across product and engineering teams to design architectures, ship reusable capabilities, and raise quality through strong engineering practices and technical leadership.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Experience architecting and shipping production large language model applications, including agentic workflows and tool integration patterns
Strong software engineering fundamentals with 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 (embeddings, semantic search, 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 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.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.

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 the 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 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
Define engineering standards for reliability, security, and safe AI operation across the platform lifecycle
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.
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.