Mid-Level Artificial Intelligence Engineer

Job Description Roles & Responsibilities 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. Desired Candidate Profile A bachelor's degree in computer science, artificial intelligence, data science, or a related field is required. A master's degree in computer science, artificial intelligence, data science, or a related field is desirable. All candidates must submit a copy of the required educational degree. A minimum of 5 years of professional experience in AI/ML engineering or software engineering with a demonstrated AI focus is required. Hands-on experience designing and deploying agentic systems using multi-agent frameworks (LangGraph, AutoGen, CrewAI, or equivalent) is required. Proficiency in Python and experience with LLM APIs (OpenAI, Anthropic, Mistral, or open-source alternatives via Ollama or HuggingFace) is required. Demonstrated knowledge of the current LLM and AI landscape, including frontier model capabilities, benchmarks, and limitations, is required. Experience with RAG architectures, vector databases (FAISS, Weaviate, Qdrant, or equivalent), and semantic search is desirable. Familiarity with model fine-tuning, RLHF, and evaluation methodologies is desirable. Knowledge of Arabic NLP and multilingual model handling is desirable. Company Industry NGOSocial ServicesCommunity ServicesNon-Profit Department / Functional Area IT Software Keywords Mid-Level Artificial Intelligence Engineer Get real-time job updates only on our App