Cloud ML Ops Engineer – Azure

Full Description

We have an urgent requirement for Cloud ML Ops Engineer – Azure | LLM Ops | RAG is required for our banking client in Abu Dhabi, UAE

Strong experience Azure ML Ops (Production) – Model deployment, versioning, monitoring on Azure ML is THE MUST

Strong experience on LLM Ops with Azure OpenAI – Real production experience managing LLM workloads is THE MUST

Strong experience on RAG Implementation –Hands-on RAG using Azure AI Search + embeddings + LLMs is THE MUST

Experience on CI/CD on Azure – ML/LLM CI/CD with Azure Functions is MUST

We are seeking a skilled Cloud ML Ops Engineer with strong expertise in Azure-based ML/LLM Ops, including OpenAI and Retrieval-Augmented Generation (RAG) workloads.

The role focuses on building, deploying, and managing scalable, reliable, and cost-optimized ML and LLM pipelines across Azure cloud services.

Key Responsibilities

• Design, implement, and manage end-to-end ML/LLM Ops pipelines on Microsoft Azure

• Deploy, version, and monitor ML models and Large Language Models (LLMs), including Azure OpenAI workloads

• Build and maintain CI/CD pipelines for ML and LLM applications

• Implement and support RAG (Retrieval-Augmented Generation) architectures using Azure AI Search and OpenAI models

• Ensure reliability, scalability, performance, and cost optimization of ML/LLM systems

• Set up monitoring, logging, and observability for production ML/LLM pipelines

• Collaborate with data scientists, engineers, and product teams to operationalize AI solutions

• Ensure security, compliance, and best practices across Azure cloud environments

Mandatory Technical Skills

• Strong hands-on experience with Azure ML

• Experience deploying and managing Azure OpenAI / LLM Ops

• Proven experience with RAG pipelines and Azure AI Search

• Experience with CI/CD for ML/LLM workloads

• Hands-on experience with:

• Azure Functions

• Azure Container Apps

• Azure AI Search

• Model deployment, versioning, monitoring, and performance tuning

• Observability tools and practices for ML systems