Agentic AI Engineer

webook.com · Remote · Location not stated

Location not statedExperience not statedPublished 14 May 2026Check carefully

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First recorded by Psst: 3 November 2026. Sign in with Google to continue to the employer. A free account is enough.

The 20-second read

Experience
Experience not stated
Where you can live
Location not stated
Skills and tools
python, aws, gcp, docker, kubernetes, ticketing
Job type
Full-time

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Job details

Where you can work
Location not stated
Employment type
Full-time
Advertised pay
Not stated by the employer
Experience
Experience not stated
Employer publication date
14 May 2026
First recorded by Psst
3 November 2026
Last checked at the source
3 November 2026

About the job

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.

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