Principal AI Engineer
Tasks
- Build reference implementations for high code agents
- Create technical documentation and code samples
- Define agent lifecycle management standards
- Design and implement agent orchestration frameworks
- Develop MCP server development best practices
- Develop internal SDKs for Gemini usage
- Drive secure scalable orchestration layer delivery
- Establish multi agent collaboration patterns
- Establish safety guardrails for agent workflows
- Evaluate and benchmark agent model performance
- Implement caching streaming and batching strategies
- Implement logging tracing telemetry and evaluation pipelines
- Implement secure agent integration patterns for enterprise systems
- Lead Gemini integration via Vertex AI
- Optimize inference latency parallelization and cost
- Optimize prompt engineering for structured outputs
- Partner on secure data handling RBAC and compliance alignment
- Provide architectural reviews for AI enabled applications
- Support engineering teams adopting AI developer tools
Perks/Benefits
- N/A
Skills/Tech-stack
API Design | Agent Frameworks | Authentication | Authorization | Batching | Caching | Cloud Native | Cloud Native Architecture | Distributed Systems | Embeddings | Evaluation Pipelines | Gemini API | Go | JSON Schema | Java | JavaScript | Kubernetes | LLM | Latency optimization | MCP | Microservices | Model Context Protocol | OpenAI API | Prompt engineering | Python | RAG | RBAC | Streaming | Structured Output | Token efficiency | Tool-Calling | TypeScript | Vector Databases | Vertex AI
Education
N/A
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