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Mondia Group

Agentic AI Agent

Place: Dubai, Dubai, United Arab Emirates
Posted: Yesterday

Applies on apply.workable.com

The role

Be part of shaping how AI transforms how we work. You’ll scout emerging AI technologies, pilot new tools, and coordinate cross-departmental adoption projects. A key focus will be managing and enhancing our internal Knowledge Hub — ensuring information flows seamlessly and employees can leverage AI to its fullest.

Design and implement agentic architectures — multi-step planners, ReAct loops, tool-use orchestration, and memory systems

Build reliable agent pipelines that handle ambiguity, recover from failures, and know when to escalate to humans

Integrate LLMs (Claude, GPT, open-source models) with external tools, APIs, databases, and enterprise systems

Develop evaluation frameworks to measure agent reliability, accuracy, cost, and latency in production

Implement guardrails, safety layers, and human-in-the-loop controls for autonomous systems

Optimize for real-world constraints: token costs, latency budgets, rate limits, and context window management

Collaborate with product and engineering teams to identify high-value automation opportunities across the digital entertainment portfolio

Contribute to internal tooling and frameworks that accelerate agent development across the organization

Requirements

5+ years of software engineering experience, with at least 1-2 years focused on LLM-based systems

Hands-on experience building agentic systems — not just prompt engineering, but orchestration, tool use, and state management

Strong proficiency in Python; familiarity with TypeScript is a plus

Deep understanding of LLM APIs (Claude, OpenAI, etc.), including function calling, structured outputs, and streaming

Experience with at least one agent framework (LangGraph, CrewAI, AutoGen, custom frameworks)

Solid software engineering fundamentals: testing, CI/CD, observability, version control

Experience with RAG pipelines, vector databases (Pinecone, Weaviate, pgvector), and retrieval systems

Experience with MCP (Model Context Protocol) or similar tool-integration standards