Agentic AI Engineer
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Key skills for this role
About the Role
Webook is hiring an Agentic AI Engineer to build multi-step, tool-using agents that plan, retrieve, act, and verify. The role involves designing agent architectures, integrating tools/APIs, and ensuring robust execution with safety and observability.
Key Skills for This Role
Responsibilities
- Architect agent loops (planning, memory, retrieval, tool use, self verification)
- Implement tools (functions/APIs/DB queries/files) and MCP style interfaces
- Combine RAG with agents: chunking, embeddings, retrieval, reranking, grounding
- Add guardrails: execution sandboxes, permissions, rate limits, PII policies
- Build evaluation for agents (task success, autonomy depth, recovery rate)
- Optimize for reliability, determinism where needed, and cost
Requirements
- Strong Python engineering skills with async patterns and error handling
- Production experience with LangChain/CrewAI or similar agent frameworks
- Practical RAG skills: vector DBs, indexing pipelines, retrieval quality tuning
- Systems thinking: queues, retries, idempotency, caching, concurrency control
- Knowledge of security and safety for agents (prompt injection, tool scoping, least privilege)
Full Job Posting
Role Overview
- Build multi step, tool using agents that plan, retrieve, act, and verify. You'll design agent architectures, integrate tools/APIs, and deliver robust execution with safety and observability.
Key Responsibilities
- Architect agent loops (planning, memory, retrieval, tool use, self verification).
- Implement tools (functions/APIs/DB queries/files) and MCP style interfaces.
- Combine RAG with agents: chunking, embeddings, retrieval, reranking, grounding.
- Add guardrails: execution sandboxes, permissions, rate limits, PII policies.
- Build evaluation for agents (task success, autonomy depth, recovery rate).
- Optimize for reliability, determinism where needed, and cost.
Required Skills
- Production experience with LangChain/CrewAI (or similar) and function/tool calling.
- Strong Python engineering; async patterns; robust error handling.
- Practical RAG skills: vector DBs, indexing pipelines, and retrieval quality tuning.
- Systems thinking: queues, retries, idempotency, caching, concurrency control.
- Security & safety for agents (prompt injection, tool scoping, least privilege).
Nice to Haves
- GCP/AWS, Docker/Kubernetes; message buses/streams.
- LLM evaluation frameworks; synthetic data generation.
- Graph based planning, constraint solvers, or program of thought techniques.
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