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Software Engineer III - AIML

JPMorgan Chase
Virginia, USA
Full-time
Mid · 3+ years experience
Onsite
Discovered 4 days ago
dockergrpckafkakubernetesopentelemetrypython

Full Job Posting

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.

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