Principle AI Engineer
Tasks
- Benchmark agent model performance across workloads
- Build reference implementations for high code agents
- Create reusable platform documentation libraries and code samples
- Create secure integration patterns for enterprise systems
- Define multi agent collaboration patterns
- Design and implement agent orchestration frameworks
- Develop internal SDKs for Gemini usage
- Drive MCP server development and API mediation
- Establish agent lifecycle management standards
- Establish safety guardrails for hallucination and prompt injection
- Implement authentication authorization and RBAC enforcement patterns
- Implement caching streaming and batching strategies
- Implement logging tracing and telemetry pipelines
- Lead Gemini integration via Vertex AI
- Optimize inference latency and parallelization
- Optimize prompt engineering and token efficiency
- Provide architectural review for AI enabled applications
Perks/Benefits
- N/A
Skills/Tech-stack
API Design | Agent Frameworks | Authentication | Authorization | Batching | Caching | Cloud Native | Cloud Native Architecture | Embeddings | Evaluation Pipelines | Function Calling | Gemini API | Go | JSON Schema | Java | Kubernetes | Langchain | Llamaindex | Logging | MCP | Microservices | Model Context Protocol | OpenAI API | Policy enforcement | Prompt engineering | Python | RAG | RBAC | Streaming | Structured Output | Telemetry | Token efficiency | Tool-Calling | Tracing | TypeScript | Vector Databases | Vertex AI
Education
N/A
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