Machine Learning Engineer

Role- ML Engineer Location- London, UK (Hybrid) Job Description: Key Responsibilities • Design, develop, and deploy scalable Machine Learning and Generative AI solutions using Python and Azure AI services. • Build and manage end-to-end ML/LLM pipelines using Azure ML, Azure AI Foundry, Azure OpenAI, and Databricks. • Develop and deploy production-grade LLM applications including fine-tuning, prompt engineering, inference optimization, and monitoring. • Implement and maintain MLOps workflows, CI/CD pipelines, and model lifecycle management processes. • Work with Azure services including AKS, ADF, Synapse, Azure Storage, and containerized deployments. • Monitor model performance, drift detection, scalability, reliability, and operational efficiency. • Collaborate with cross-functional teams including Data Engineering, DevOps, Product, and Architecture teams. • Implement best practices for version control, reproducibility, governance, monitoring, and AI security. • Troubleshoot and optimize ML/AI systems in production environments. Required Skills & Experience • 8+ years of experience in Machine Learning Engineering / MLOps. • Strong programming experience in Python with ML frameworks and Azure SDKs. • Hands-on experience with: • Azure ML • Azure AI Foundry • Azure OpenAI • AKS (Azure Kubernetes Service) • Databricks • Azure Data Factory (ADF) • Azure Synapse • Azure Storage • Experience deploying and monitoring LLMs in production environments. • Strong understanding of: • Fine-tuning • Prompt Engineering • Inference Optimization • Generative AI • LLMOps • Experience with CI/CD pipelines using Azure DevOps and GitHub Actions. • Strong knowledge of Docker and containerized deployments. • Familiarity with MLOps best practices including: • Version Control • Experiment Tracking • Reproducibility • Monitoring & Logging • Excellent problem-solving, communication, and collaboration skills.