Mid-Level Artificial Intelligence Engineer

Client of United Nation careers

Work Assignment

• Agentic AI System Design and Development
  
  • Architect and implement multi-agent systems using frameworks such as LangGraph, AutoGen, CrewAI, or equivalent, capable of orchestrating complex reasoning, retrieval, and action pipelines.
  • Design and deploy Retrieval-Augmented Generation (RAG) systems with advanced retrieval strategies (hybrid search, re-ranking, contextual compression) for knowledge-intensive tasks.
  • Implement tool-use and function-calling patterns enabling agents to interact with external APIs, databases, and computational tools.
  • Develop agent evaluation frameworks, including automated benchmarking, failure analysis, and iterative improvement loops.
• LLM Integration and Optimization
  
  • Integrate and benchmark state-of-the-art LLMs (proprietary and open-source) across use cases including information extraction, summarization, classification, and question answering on Arabic and multilingual content.
  • Apply prompt engineering, structured output generation, and chain-of-thought techniques to maximize model reliability and task performance.
  • Explore and implement fine-tuning strategies (LoRA, QLoRA, full fine-tuning) where domain adaptation is required.
  • Monitor and optimize inference costs, latency, and throughput for production deployments.
• AI Infrastructure and API Development
  
  • Design and implement RESTful and streaming APIs to expose AI capabilities to internal systems and the ADP platform.
  • Containerize AI services using Docker and manage deployments in line with organizational infrastructure standards.
  • Maintain documentation, versioning, and model cards for all deployed AI systems.
• Research, Innovation, and Collaboration
  
  • Continuously monitor and evaluate emerging AI research, frameworks, and tooling to identify adoption opportunities aligned with DSDSD's mission.
  • Collaborate with data engineers, ML engineers, and domain experts to integrate AI capabilities into end-to-end data products.
  • Prepare technical reports, demonstrations, and presentations to communicate AI system design and findings to both technical and non-technical stakeholders.