AI Engineer

The InstituteTechnology Innovation Institute (TII) a publicly funded research institute based in Abu Dhabi, the United Arab Emirates. It is home to a diverse community of leading scientists, engineers, mathematicians, and researchers from across the globe, transforming problems and roadblocks into pioneering research and technology prototypes that help move society ahead.AI and Digital Science Research Centre The Artificial Intelligence and Digital Science Research Centre (AIDRC) is the machine learning powerhouse of TII. We work closely with other research centers to harness the full potential of AI across multiple domains — from healthcare and cybersecurity to government and advanced technology systems. We incorporate core elements of intelligence (perception, sensing, planning, and language) in the ideation, design, and prototyping of next-generation systems with human-like intelligence. We build advanced AI computing and scalable AI-based software stacks and hardware systems to deliver significant enhancements in systems infrastructure. Our AI researchers, scientists, and engineers collaborate to ensure innovative outcomes, from AI theory to AI technologies towards better intelligence.Duties & Responsibilities:Design and build agentic AI systems (multi-agent workflows, tool use, planner and executor patterns) on top of Falcon and other foundation models, applied to enterprise and government use cases.Architect end-to-end RAG pipelines, covering ingestion, chunking, embedding, hybrid retrieval, reranking, and grounded generation across structured and unstructured data sources.Own model serving and inference optimization with vLLM (or equivalent frameworks), including batching, KV caching, quantization, and throughput tuning to meet latency and cost targets.Lead prompt engineering, model selection, and lightweight fine-tuning (PEFT, LoRA, SFT) where it measurably improves application quality.Build rigorous evaluation frameworks and online and offline benchmarks to measure quality, safety, latency, and cost of deployed AI systems, and act on the results.Investigate and mitigate AI failure modes such as hallucination, retrieval gaps, prompt injection, and unsafe outputs, and codify the fixes into reusable patterns.Partner with full-stack engineers, researchers, and product teams to translate business problems into well-scoped AI solutions, then ship them to production and iterate based on real usage.Contribute to internal AI frameworks, libraries, and engineering best practices that accelerate future deployments across the centre.Job Specification:Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related discipline.2 to 8 years of hands-on experience building and deploying AI or machine learning systems in production environments.Strong Python skills, with deep experience in modern AI and ML libraries such as PyTorch, Hugging Face Transformers, LangChain or LlamaIndex, and serving frameworks such as vLLM or TGI.Proven experience with agentic frameworks and design patterns (function calling, tool use, multi-agent orchestration) and with building production RAG systems.Hands-on experience with model serving and inference optimization, including batching, KV caching, and INT4/INT8 quantization for LLMs.Practical experience with prompt engineering, model evaluation, and lightweight fine-tuning (PEFT, LoRA, or SFT) of foundation models.Solid software engineering foundations, including Python API development, version control, testing, and code review.Working knowledge of containerization (Docker), CI/CD, and basic MLOps practices such as model and prompt versioning and observability.Familiarity with vector databases (e.g., Milvus, Qdrant, pgvector, Weaviate) and embedding-based retrieval at scale.Strong communication skills and the ability to engage directly with internal stakeholders and enterprise clients in English (Arabic is a plus).Additional Skills:Patents.Publications in top-ranking conferences and journals, such as ICML, NeurIPS, ICLR, KDD, VLDB, AAAI, and ACL, to cite a few.Experience deploying AI systems in regulated industries (government, financial services, defence) or in Arabic-language contexts.Contributions to open-source AI projects.