Principal AI Engineer
Tasks
- Build and curate agent and skill catalog
- Build evaluation harness for agents
- Build secure connectors to business systems
- Create enterprise context layer
- Define platform standards and guardrails
- Design and ship production agentic systems
- Develop reusable skills and agent templates
- Enforce AI policies and responsible AI guardrails
- Establish audit logging and telemetry
- Implement cost and value controls
- Implement human-in-the-loop controls
- Implement observability for deployed agents
- Implement planner executor flows
- Implement retrieval-augmented generation
- Implement structured output
- Implement tool and function calling
- Lead internal showcases and demos
- Map workflows and identify high leverage agent opportunities
- Mentor engineers on agentic patterns
- Publish documentation standards and connectors
- Run agent review and promotion gates
- Set architecture patterns and engineering standards
- Stand up agentic AI platform
- Triage ideas between agentic AI and conventional automation
Perks/Benefits
Skills/Tech-stack
APIs | Agent Frameworks | Agent Orchestration | Anthropic Claude | Audit Logging | Azure | CI/CD | Data Classification | Data Pipelines | Drift Detection | Embeddings | Human-in-the-loop | JavaScript | LLM APIs | Least Privilege | MCP | Microservices | Model Context Protocol | Model routing | Monitoring | Multi-Agent | Multi-agent orchestration | Observability | Orchestration | Prompt engineering | Python | Retrieval-Augmented Generation | SQL | Semantic models | Snowflake | Structured Output | Telemetry | The Loop | Tool use | TypeScript | Vector Search | Version control
Education
Roles
AI | AI Engineer | Engineer | Principal | Principal AI Engineer
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