Principal Engineer - Architect (AI Agents)
Tasks
- Build evaluation harness
- Catch silent failures
- Deploy live agents
- Design agent runtime
- Drive engineering roadmap
- Ensure observability
- Implement offline evaluations
- Implement online evaluations
- Instrument tracing and monitoring
- Mentor engineers
- Monitor live agents
- Own cloud infrastructure for AI agents
- Own system architecture end to end
- Recover failed agents
- Review engineering work
- Set technical direction
- Use LLM as judge scoring
Perks/Benefits
Skills/Tech-stack
AI Agents | AWS | Agent memory | Agent workflows | Azure | Cause analysis | Containers | Distributed Systems | Docker | Evaluation Frameworks | Function Calling | GCP | Go | Hermes | Incident Response | Infrastructure as Code | Kubernetes | LLM Evaluation | LLM integration | LLM-as-judge | Language Models | Large Language Models | MCP | Monitoring | Multi Provider LLM | Multi Provider LLM Integration | Multi provider | Multi-Agent | Multi-agent workflows | Multi-tenancy | Observability | Ollama | OpenAI | OpenClaw | Prompt orchestration) | RAG | Root Cause Analysis | Root cause | Structured Output | Terraform | Tool orchestration | Tracing | VLLM | “as-code”
Education
N/A
Roles
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