Senior ML Engineer
Tasks
- Add evals and observability to agents
- Add multi tenant gating
- Build and maintain low latency ML API
- Create golden transcripts and rubric judges
- Create unit tests for tools
- Define model shipping playbook for LLM callable tools
- Deploy tools via agent runtime
- Design tool schemas and handlers
- Harden ML API for low latency traffic
- Implement prompt versioning
- Implement structured error contracts
- Lead NBA production AI roadmap
- Mentor data engineers on ML AI patterns
- Own production ML and AI agent systems
- Partner with platform team on agent infrastructure
- Provide agent observability and monitoring
- Run regression suites on prompt or model changes
- Set up agent evaluation framework
- Ship agent tool contracts end to end
Perks/Benefits
- Home office setup
- National holidays
- Paid time off
- Remote flexibility
- Stock options
- Unforgettable off-sites
- Work trip budget
Skills/Tech-stack
AWS Lambda | Batching | BentoML | Caching | Distributed tracing | Docker | Error Handling | Evaluation | FastAPI | GitHub | Golden Transcripts | LLM Agents | LLM Tooling | Latency optimization | Multi Tenant Gating | Multi-tenant | Observability | Prompt engineering | Python | Regression testing | Schema Design | Structured data | TypeScript | Unit Testing
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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