Related skills
docker aws python kubernetes typescript📋 Description
- Partner closely with customer engineering teams to deploy, stabilize, and continuously improve AI applications in production
- Instrument and trace real-world AI workflows end-to-end, establishing baseline targets for latency, cost, quality, and reliability
- Turn production data into datasets and evaluations; define scoring rubrics and implement CI quality gates
- Build prototypes, integrations, and custom workflows that help customers operationalize evaluations and observability as part of their SDLC
- Deploy and troubleshoot Braintrust in customer environments (cloud or self-hosted), working across application, data, and infrastructure layers
- Act as the technical lead in customer engagements, running an operating cadence and feeding real-world learnings back into Product and Engineering
🎯 Requirements
- 3–7+ years of experience as a software engineer or forward-deployed / field engineer
- Strong backend or full-stack engineering skills (Python strongly preferred; TypeScript a plus)
- Hands-on experience with LLMs, APIs, or agentic workflows in production environments
- Familiarity with cloud infrastructure and deployment patterns (AWS preferred; Docker/Kubernetes a plus)
- Comfortable working directly with customers and owning technical outcomes end-to-end
- Strong communication skills and ability to translate between business needs and technical implementation
🎁 Benefits
- Medical, dental, and vision insurance
- Daily lunch, snacks, and beverages
- Flexible time off
- Competitive salary and equity
- AI Stipend
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