We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Software Engineer III at JPMorganChase within Corporate AIML Data Platforms- Chief Data and Analytics Office , you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for contributing to critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives—with a focus on Go/Python based backend services , to support LLM-enabled experiences. Job responsibilities Execute creative software solutions, design, development, and technical troubleshooting for LLM-enabled applications and platforms . Design, build, and maintain backend services in Go , delivering secure, high-quality production code with strong testing discipline. Build gRPC services defined with Protobuf , including API design, schema evolution, and compatibility/versioning practices. Implement bidirectional streaming gRPC endpoints for LLM workloads (e.g., interactive sessions, token/partial-response streaming, incremental results, cancellations, backpressure/flow control). Integrate LLM services with supporting systems (authn/z, rate limits/quotas, caching, queues/streams, persistence) and partner with platform/SRE teams as needed. Review and debug SDK and service integrations; participate in code reviews and contribute to shared engineering standards. Instrument services with strong observability (metrics/logging/tracing), and contribute to runbooks and operational readiness (alerts, dashboards, on-call support as applicable). Identify recurring issues and contribute fixes and automation to improve reliability, latency, and operational stability. 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. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 3+ years applied experience Proficiency in Go for backend/service development, including concurrency patterns (goroutines/channels), context usage, and performance troubleshooting. Working knowledge of Python for supporting services, internal tooling, automation, evaluation harnesses, or data/ETL tasks. Hands-on experience with gRPC and Protobuf in production, including API design and backward/forward compatibility considerations. Experience building and operating microservices/APIs with a production mindset (monitoring, debugging, incident support, and continuous improvement). Proficiency in automation and continuous delivery methods; strong unit/integration testing discipline. Proficient in secure coding practices and core SDLC activities (design, build, test, deploy, operate). 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. Preferred qualifications, capabilities, and skills Experience building LLM streaming experiences end-to-end (token/partial-response streaming, tool/function calling orchestration, session state management, safety/guardrail hooks). Familiarity with OpenTelemetry and tracing/metrics patterns for streaming RPCs (correlation IDs, span links, per-message metrics). Experience with Kubernetes/Docker and operating services in containerized environments. Messaging/streaming systems (Kafka/RabbitMQ/SQS), caching (Redis), and patterns for async orchestration alongside streaming. Security best practices for RPC systems: mTLS, authn/z, secrets management, secure-by-default API design, abuse prevention (rate limiting, payload limits). Understanding of cost management and spend alerting for AI/LLM workloads.