Senior AI Engineer
Tasks
- Build high code agent reference implementations
- Create technical documentation and code samples
- Define agent lifecycle management standards
- Design and implement agent orchestration frameworks
- Develop MCP server patterns and API mediation
- Develop reusable Gemini SDKs and components
- Enable teams using Gemini Code Assist and internal AI platforms
- Ensure secure data handling RBAC and compliance alignment
- Establish multi agent collaboration patterns
- Establish safety guardrails and policy enforcement
- Evaluate and benchmark agent and model performance
- Implement caching streaming and batching strategies
- Implement observability logging tracing telemetry and evaluation
- Implement secure agent integrations with enterprise systems
- Lead Gemini integration via Vertex AI
- Optimize inference latency parallelization and cost
- Optimize prompt engineering token efficiency and grounding
- Perform architectural reviews for AI enabled applications
Perks/Benefits
- N/A
Skills/Tech-stack
API Design | API Integration | Agent Frameworks | Authentication | Authorization | Backend architecture | Cloud Native | Distributed Systems | Embeddings | Evaluation Pipelines | Gemini API | Go | Java | Kubernetes | LLM Architecture | Langchain | Llamaindex | Logging | Microservice Security | Microservices | Model Context Protocol | OpenAI API | Orchestration frameworks | Prompt engineering | Python | RAG | RBAC | Retrieval strategies | Secure API | Secure API integration | Telemetry | Tool-Calling | Tracing | TypeScript | Vector Databases | Vertex AI
Education
N/A
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