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.