Senior / Principal AI & Machine Learning Engineer

**\*\*HIRING\*\*** **Role:** Senior / Principal AI \& Machine Learning Engineer (Product\-Focused) **Location:** UAE (Hybrid / On\-site depending on client) **Salary:** AED 40,000 – 70,000\+ Per Month (depending on seniority, impact, and ownership) **Type:** Permanent / Senior Contract Options **Overview** I am partnering with a high\-growth, product\-led organisation in the UAE building next\-generation AI systems across large\-scale data platforms, applied machine learning, and LLM\-driven products. This is not an “analytics support” or research\-only role. This is a deeply hands\-on engineering position for someone who builds, and scales production AI systems used by real users in real\-time environments. I am looking for Senior to Principal\-level AI/ML Engineers who operate as technical owners of AI products — from model design through to deployment, optimisation, and ongoing performance in production. **What You’ll Be Doing** You will act as a technical leader and builder within a cross\-functional AI product team, responsible for taking machine learning systems from concept through to production at scale. **Core responsibilities include:** * Designing and building end\-to\-end machine learning systems in production environments * Developing and deploying scalable AI/ML models (supervised, unsupervised, deep learning, and generative AI use cases) * Leading architecture decisions for ML pipelines, feature stores, training infrastructure, and inference systems * Building and optimising LLM\-based applications (RAG systems, fine\-tuning, prompt engineering, evaluation pipelines) * Owning MLOps practices: CI/CD for ML, model versioning, monitoring, drift detection, and retraining pipelines * Collaborating closely with product, engineering, and data teams to translate business problems into scalable AI solutions * Improving model performance, latency, cost efficiency, and reliability in production * Mentoring engineers and setting technical standards across AI/ML teams * Contributing to platform\-level decisions around data, AI infrastructure, and cloud architecture **Candidate Profile** * 7–15\+ years' experience in software engineering, data science, or ML engineering * Strong background in product companies, scale\-ups, or enterprise AI platforms * Previously built production\-grade AI systems, not just notebooks or POCs * Comfortable owning systems end\-to\-end (data → model → deployment → monitoring) * Often ex\-FAANG, major tech firms, AI startups, or high\-performing regional tech companies * Strong engineering mindset (not just modelling) * Product\-first engineering, not research\-only profiles * Strong Python engineering with systems thinking * Used to fast iteration and deploying models into live environments * Comfortable working directly with stakeholders and product owners **Core Technical Stack** **Machine Learning / AI** * PyTorch * TensorFlow * XGBoost / LightGBM * Hugging Face ecosystem (Transformers, Datasets, Diffusers) * OpenAI / Anthropic APIs (production LLM integration) * LangChain / LlamaIndex (agentic workflows, RAG systems) **LLM / GenAI Stack (very important in 2026\)** * RAG architectures (vector DB \+ retrieval pipelines) * Embedding models (OpenAI, Cohere, open\-source models) * Vector databases: Pinecone, Weaviate, Milvus, FAISS * Fine\-tuning frameworks (LoRA, PEFT) * Evaluation frameworks (RAGAS, custom eval pipelines) **MLOps / Production** * Docker, Kubernetes * MLflow / Weights \& Biases * Airflow / Dagster / Prefect * CI/CD tools (GitHub Actions, GitLab CI) * Model monitoring (Evidently AI, Arize, custom observability stacks) **Cloud Platforms** * AWS (SageMaker, EKS, S3, Lambda) or Azure ML / Azure Databricks **Data Stack** * Databricks * Spark (PySpark essential) * Delta Lake / Iceberg / Lakehouse architectures