About the role
from listingElevate your career as a Back-end Software Engineer working on AI and ML solutions.
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.