AI Engineer - Lead Software Engineer

About the role

As an 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.

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.

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.

Qualifications

• 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

Skills

• Knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities

Benefits

Our total rewards package includes base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more.