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4 mo agofound 5 d ago
Agentic AI Engineer
What the posting is about
Design and build multi-step AI agents using LangChain/CrewAI. Architect agent loops and integrate tools/APIs. Combine RAG with agents and ensure safety and observability. Optimize for reliability and cost.
Read out of the posting
Levelmid
Experience asked3+ years
EmploymentFull time
LocationAmman, Jordan
RemoteYes
Visa sponsorshipNot stated
SalaryNot published, and most postings do not
Posted2026-05-14
Found viaworkable, direct from their system
We saw it 5 months after it went up.
The posting, as the company wrote it
Employment: Full-time
Experience: Mid-Senior level
Education: Bachelor's Degree
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 Saudi’s #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. 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.
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.
Copied from webook.com’s own board, not rewritten. Original ↗
Also open at webook.com
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