Agentic AI Engineer
At a glance
Mid-level AI Engineering role at webook.com. Remote, Jordan · full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Design agent workflows that plan, use memory and retrieval, call tools, then check their own results
- Connect agents to functions, APIs, database queries and files, including MCP-style interfaces
- Pair retrieval-augmented generation with agents through chunking, embeddings, reranking and grounded answers
- Set agent safety controls, then measure task completion, autonomy and recovery
- Improve dependable execution and control costs, using consistent results where required
What you bring
- You have production experience with LangChain, CrewAI or a comparable framework, including tool calls
- You bring strong Python skills, including asynchronous work and resilient error handling
- You have hands-on RAG experience with vector databases, indexing and retrieval tuning
- You understand agent security risks such as prompt injection, and how to limit tool access
Who this fits
This mid-level remote role suits an engineer focused on building and securing tool-using AI agents. The position is based in Amman, Jordan.
From the employer
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.