Principal AI Engineer (R&D)
Tasks
- Architect agent memory layer
- Build agent runtime registry and observability
- Build harnesses and CI gates for prompts models and tools
- Create agent tool interfaces
- Define memory retention and decay policies
- Design Agentic AI systems
- Design scalable backend services and RESTful APIs
- Develop prompting strategy and guardrails
- Enforce RBAC and tenant isolation for memory
- Establish evaluation discipline with regression evals
- Evaluate and integrate local LLMs
- Implement agent loops and harnesses
- Implement authentication authorization RBAC and validation
- Implement conversation history and context chaining
- Implement typed agent frameworks with telemetry
- Lead internal agent platform libraries
- Maintain CI CD with Git GitLab and Docker
- Perform performance tuning code reviews and documentation
- Set up caching queues and vector storage
Perks/Benefits
- N/A
Skills/Tech-stack
Agent memory | Authentication | Authorization | CI/CD | Caching | Context Chaining | Docker | Episodic memory | FastAPI | Git | GitLab CI | GitLab CI/CD | Input validation | JavaScript | LLM Evaluation | Llama.cpp | Local LLMs | MongoDB | Multi-tenant | Multi-tenant Security | Observability | Ollama | PostgreSQL | Prompt engineering | Pydantic | Pytest | Python | Queues | RBAC | REST APIs | Red Teaming | Redis | Regression testing | SQLAlchemy | Structured data | Telemetry | Tenant Security | Testing | Tool integration | TypeScript | VLLM | Vector Databases
Education
N/A
Roles
AI | AI Engineer | Engineer | Principal | Principal AI Engineer
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