Software Engineer III - Java / Python, AI & ML

b Overview /b p 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. /p b Responsibilities /b ul li Lead end-to-end initiatives from requirements to production support with strong ownership /li li Design and implement AI solutions using LLMs and agent patterns, including prompting, tool calls, retrieval, routing, and memory/state management /li li Build guardrails, evaluation frameworks, monitoring pipelines, and cost/latency optimizations for production LLM systems /li li Design, build, and operate REST and gRPC APIs and microservices with OpenAPI and Protobuf contracts, ensuring backward compatibility, authentication, rate limiting, and observability /li li Apply resilience engineering patterns (timeouts, retries, circuit breakers) for production-grade behavior /li li Develop and maintain Python services with solid packaging, dependency management, and architectural standards /li li Own data design and complex SQL optimization for performance and reliability /li li Build infrastructure as code with Terraform, containerized deployments via Kubernetes, and CI/CD across multi-cloud environments /li li Drive engineering excellence in code quality, testing, performance, reliability, and incident analysis /li li Mentor engineers, provide technical guidance, and establish standards for delivery and engineering practices /li li Promote enterprise AI-assisted development practices (code review, testing strategies, incident analysis) and reusable patterns across the team /li li Leverage SDLC toolchain and enterprise AI-assisted development to improve automation value and validation of AI outputs /li /ul b Key requirements /b ul li Formal training or certification in software engineering concepts and advanced applied experience /li li Proven track record delivering end-to-end software with strong ownership /li li Strong Python software engineering skills for production-grade services and automation /li li Strong understanding of relational databases, SQL, and query optimization /li li Experience building AI solutions with large language models in production (QA, safety, observability, cost management) /li li API and microservices engineering experience (design, security, performance, observability) /li li Hands-on multi-cloud experience (AWS preferred) with distributed systems fundamentals /li li Strong Terraform skills for IaC, environment management, and remote state /li li DevOps practices including CI/CD, Git workflows, and Kubernetes deployments /li li Experience with enterprise AI-assisted development tools and evaluating AI outputs for correctness, security, and performance /li li Understanding of responsible AI use in engineering workflows /li /ul ul li ownership and accountability /li li mentorship and technical leadership /li li cross-functional collaboration /li li LLMs and agent architectures /li li Prompting strategies and tool calling /li li Retrieval, routing, and memory/state management /li /ul