Azure AI Engineer

Azure AI Engineer - Abu Dhabi - 12 Month Contract Location: Abu Dhabi, UAE – OnsiteContract: 12 monthsSalary: AED 18,000–20,000 per monthBenefits: Employment visa and healthcare providedOur Consultancy client is seeking an experienced Azure AI/ML Engineer to join a major healthcare technology programme in Abu Dhabi.You will be responsible for designing, building, deploying and supporting production-grade AI, machine learning, generative AI, computer vision and agentic workflow solutions. This role will focus on transforming data science prototypes and clinical innovation concepts into secure, scalable and reliable AI services integrated with healthcare data platforms, clinical systems and digital applications.Required ExperienceStrong production-level software engineering experience using Python.Experience with an additional programming language such as C#, Java, Scala, Go or TypeScript.Hands-on experience building, deploying and supporting production AI, ML or GenAI solutions.Strong knowledge of frameworks such as PyTorch, TensorFlow, Keras, scikit-learn and Hugging Face.Experience with MLOps or LLMOps, including CI/CD, MLflow, model registries, experiment tracking and model monitoring.Azure cloud experience, ideally including:Azure Machine LearningAzure AI FoundryAzure OpenAIAKS/KubernetesDockerAzure DatabricksMicrosoft FabricAzure Data LakeAzure Key VaultTerraform or other infrastructure-as-code toolsExperience with RAG, embeddings, vector databases, prompt management, guardrails and agent orchestration.Key ResponsibilitiesDesign and deliver secure, scalable and maintainable production AI and machine learning services.Develop batch, real-time, streaming, API-based and event-driven AI architectures.Build reusable ML platform capabilities, including experiment tracking, feature stores, vector stores, model registries and automated deployment pipelines.Containerise, deploy and scale machine learning, deep learning, computer vision and generative AI models.Optimise model performance, latency, GPU utilisation, throughput and cloud costs.Build ML-ready data pipelines using SQL, Spark, lakehouse and streaming technologies.Develop secure LLM applications using Azure OpenAI, Azure AI Foundry, RAG, embeddings, vector search and agentic AI.Implement MLOps and LLMOps practices, including CI/CD, GitOps, automated testing, model monitoring, retraining triggers and rollback strategies.Deploy computer vision and multimodal AI solutions for medical imaging, clinical video and document processing.Develop APIs, microservices, SDKs and integration services that embed AI outputs into clinical and operational applications.Monitor model performance, data drift, hallucination, grounding, latency, service health and cost.Implement responsible AI, security, privacy, audit logging and healthcare data protection controls.Work closely with data scientists, clinicians, cloud engineers, cybersecurity specialists and product teams.