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Software Engineer III - AIML

Job in McLean, Fairfax County, Virginia, USA
Listing for: JPMorgan Chase & Co.
Full Time position
Listed on 2026-10-24
Job specializations:
  • Software Development
    Backend Developer, DevOps, Software Engineer
Salary/Wage Range or Industry Benchmark: 120000 - 190000 USD Yearly USD 120000.00 190000.00 YEAR
Job Description & How to Apply Below

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 JPMorgan Chase 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, back pressure/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 (go routines/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…
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