Lead Software Engineer – Java / Python, AI & ML

Overview

As Lead Software Engineer at JPMorganChase you will drive AI-powered, cloud-native systems from discovery to production, surrounding multi-cloud environments with robust APIs and secure, scalable architectures. You’ll own end-to-end delivery and shape engineering standards while solving complex, high-impact problems at scale. You will work closely with cross-functional teams to implement responsible AI practices and measurable improvements in performance and reliability. This role offers a chance to influence how the firm builds and operates technology, with a focus on architecture, experimentation, and professional growth.

Responsibilities

• Lead end-to-end initiatives from requirements to production support with strong ownership
• Design and implement AI solutions using LLMs and agent patterns, including prompting, tool calls, retrieval, routing, and memory/state management
• Build guardrails, evaluation frameworks, monitoring pipelines, and cost/latency optimizations for production LLM systems
• Design, build, and operate REST and gRPC APIs and microservices with OpenAPI and Protobuf contracts, ensuring backward compatibility, authentication, rate limiting, and observability
• Apply resilience engineering patterns (timeouts, retries, circuit breakers) for production-grade behavior
• Develop and maintain Python services with solid packaging, dependency management, and architectural standards
• Own data design and complex SQL optimization for performance and reliability
• Build infrastructure as code with Terraform, containerized deployments via Kubernetes, and CI/CD across multi-cloud environments
• Drive engineering excellence in code quality, testing, performance, reliability, and incident analysis
• Mentor engineers, provide technical guidance, and establish standards for delivery and engineering practices
• Promote enterprise AI-assisted development practices (code review, testing strategies, incident analysis) and reusable patterns across the team
• Leverage SDLC toolchain and enterprise AI-assisted development to improve automation value and validation of AI outputs

Key requirements

• Formal training or certification in software engineering concepts and advanced applied experience
• Proven track record delivering end-to-end software with strong ownership
• Strong Python software engineering skills for production-grade services and automation
• Strong understanding of relational databases, SQL, and query optimization
• Experience building AI solutions with large language models in production (QA, safety, observability, cost management)
• API and microservices engineering experience (design, security, performance, observability)
• Hands-on multi-cloud experience (AWS preferred) with distributed systems fundamentals
• Strong Terraform skills for IaC, environment management, and remote state
• DevOps practices including CI/CD, Git workflows, and Kubernetes deployments
• Experience with enterprise AI-assisted development tools and evaluating AI outputs for correctness, security, and performance
• Understanding of responsible AI use in engineering workflows

• ownership and accountability
• mentorship and technical leadership
• cross-functional collaboration
• LLMs and agent architectures
• Prompting strategies and tool calling
• Retrieval, routing, and memory/state management

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