Agentic AI Engineer

AIQU is looking for a skilled Agentic AI / Machine Learning Engineer to build and deploy intelligent AI agents, autonomous workflows and production-ready AI solutions for a leading technology client in Saudi Arabia. If you have hands-on experience building Agentic AI applications, LLM-based systems, multi-agent workflows and Voice AI solutions, we'd love to connect! 📍 Location: Riyadh, Saudi Arabia 💼 Employment: 12-Month Contract (Extendable) 🎯 Experience: 4–6 Years Key Technical Skills: ✅ Agentic AI & Multi-Agent Systems: Autonomous agents, tool calling, task planning and multi-step workflows. ✅ LLM Frameworks: LangChain, LangGraph, AutoGen, CrewAI or Semantic Kernel. ✅ Python & Deep Learning: Python, PyTorch, TensorFlow, transformers, embeddings, fine-tuning and RAG. ✅ MCP (Model Context Protocol): Hands-on experience building or integrating MCP servers for AI agent tool and data access. ✅ Voice AI: Speech-to-Text (STT), Text-to-Speech (TTS) and experience supporting Saudi Arabic dialects. ✅ Redis: Caching, session/state management and real-time, low-latency AI workflows. ✅ MLOps & Deployment: Docker, Kubernetes, CI/CD, MLflow/Kubeflow and cloud deployment using AWS, Azure or GCP. Additional Skills: ⭐ Vector databases – Pinecone, FAISS, Weaviate or Milvus ⭐ REST/gRPC APIs and microservices ⭐ Model inference optimisation and production monitoring ⭐ Scalable AI application architecture Who Should Apply? We are looking for engineers with 4–6 years of AI/ML engineering experience and proven hands-on experience designing, developing and deploying production-grade Agentic AI systems. Experience in MCP integrations, Redis and Saudi Arabic Voice AI will be particularly important for this role.

4–6 Years of AI/ML engineering experience; proven hands-on experience designing, developing and deploying production-grade Agentic AI systems; experience in MCP integrations, Redis and Saudi Arabic Voice AI is particularly important.

Build and deploy intelligent AI agents, autonomous workflows and production-ready AI solutions; work with Agentic AI applications, LLM-based systems, multi-agent workflows and Voice AI solutions; ensure tool calling, task planning and multi-step workflows; integrate MCP servers for AI agent tool and data access; develop scalable AI application architecture and monitor production deployments.