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.