Job description
Contract duration: 6 months (with a potential extension) Engagement Type: Full-time Start date: August/September 2026 Location: Abu Dhabi (on-site) Role Overview We are seeking an experienced AI Engineer to design, build, and deploy production-grade AI systems — primarily LLM-powered applications, RAG pipelines, and agentic workflows — that solve real client business problems in the UAE.
The role combines applied AI expertise with solid software and data engineering foundations, with hands-on ownership from prototype to production and hypercare.
Key Responsibilities
scope the use case, build, evaluate, deploy, and hand over reliable, observable services. Build and optimize RAG pipelines: data parsing, ingestion, chunking, embeddings, vector search, re-ranking, and context-window optimization. Design and orchestrate agentic workflows for reasoning, planning, and task execution. Build evaluation and observability: define metrics (accuracy, latency, cost-per-task), run eval pipelines, and monitor systems in production.
Integrate AI components into client applications via secure backend APIs; collaborate with data, software, and Dev. Sec. Ops teams. Engineer the supporting data layer: pipelines, feature/embedding stores, and data quality for AI workloads on cloud platforms. Manage the full lifecycle: deployment, monitoring, versioning, rollback readiness, and cost/performance optimization.
Translate business problems into AI solutions and communicate model capabilities, limitations, and trade-offs clearly to clients and stakeholders. Maintain technical documentation, runbooks, and delivery artifacts across Design & Align → Build → UAT → Production & Hypercare. Candidate Requirements Demonstrated experience delivering AI solutions in an enterprise or client-facing/consulting environment.
Prior experience at management consulting firms and/or Big Tech is an advantage. Strong Python and solid software engineering fundamentals. Hands-on LLM APIs (Anthropic, OpenAI, Azure OpenAI), including tokenization, latency, and cost control. Proven RAG architecture experience with vector databases (e.g., Azure AI Search, Qdrant, Chroma, Pinecone, FAISS).
Experience with agent/orchestration frameworks (e.g., Lang. Chain, Lang. Graph, CrewAI). Evaluation and observability skills for production AI (eval pipelines, LLM-as-judge, monitoring). Working knowledge of a major cloud AI/data stack (Azure, AWS, or GCP) and MLOps tooling (e.g., MLflow, Docker). Solid data engineering skills: SQL, distributed processing (e.
g., Spark/Databricks). Strong collaboration and communication skills — able to work with both technical teams and senior client stakeholders.
Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. 3–6 years of experience building and shipping AI/ML solutions into production. Preferred certifications (nice to have): cloud AI certifications (Azure AI Engineer, AWS ML, Databricks GenAI Engineer).