Desenvolve soluΓ§Γ΅es backend em Python para um agente de atendimento com IA, integrando LLMs a sistemas externos e aprimorando APIs, fluxos conversacionais e pipelines de RAG para jornadas B2C e B2B.
Agentic AI Engineer
π Description
- Architect agent loops for planning, memory, retrieval, tool use, and self-verification.
- Implement tools and MCP-style interfaces using functions, APIs, database queries, and files.
- Combine RAG with agents through chunking, embeddings, retrieval, reranking, and grounding.
- Add safety guardrails, including execution sandboxes, permissions, rate limits, and PII policies.
- Evaluate agent task success, autonomy depth, and recovery rate.
- Optimize agent reliability, determinism, and cost.
π οΈ Requirements
- 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).
Full job description
Do you want to love what you do at work? Do you want to make a difference, an impact, and transform peoples lives? Do you want to work with a team that believes in disrupting the normal, boring, and average?
If yes, then this is the job you are looking for , webook.com is Saudiβs #1 event ticketing and experience booking platform in terms of technology, features, agility, revenue serving some of the largest mega events in the Kingdom surpassing over 2 billion in sales.
Role Overview
Build multi-step, tool-using agents that plan, retrieve, act, and verify. Youβll 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.
Requirements
- 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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