Machine Learning Engineer

ML Engineer / Senior ML Engineer – GenAI & LLM Location: London, UK (Hybrid – 3 Days Onsite) Contract Duration: 12 Months We are looking for an experienced ML Engineer / Senior ML Engineer with strong expertise in Azure, Machine Learning Engineering, LLMs, and Generative AI to join a growing AI engineering team. The role involves designing, developing, deploying, and maintaining enterprise-scale AI/ML and GenAI solutions in production environments. The ideal candidate will have hands-on experience in LLM application development, RAG pipelines, MLOps, model deployment, AI infrastructure, and scalable cloud-based ML systems. Required Skills • Strong experience with Azure / Azure ML • Hands-on experience in Machine Learning Engineering (MLE) • Expertise in LLMs (Large Language Models) • Experience in Generative AI • Strong Python and SQL skills • Experience with Docker & Kubernetes • Knowledge of CI/CD pipelines and MLOps • Experience with RAG architectures, vector databases, and embeddings • Prompt Engineering experience • Experience with LLM fine-tuning techniques such as: • LoRA • QLoRA • PEFT Nice to Have • Insurance / InsurTech domain experience Experience Required • 5–8+ years of relevant experience Key Responsibilities AI & ML Solution Development • Design, build, and deploy scalable AI/ML and Generative AI solutions. • Collaborate with business stakeholders and data scientists to develop intelligent AI systems and architectures. LLM & Generative AI Engineering • Develop enterprise-grade LLM applications and GenAI solutions. • Build and implement: • RAG pipelines • AI Agents / Agentic systems • Embedding workflows • Vector search systems • Fine-tune pretrained LLMs using LoRA, QLoRA, and PEFT techniques. • Create effective prompts and integrate LLMs with enterprise APIs and platforms. Data Engineering & Feature Engineering • Design and maintain robust ETL/ELT pipelines. • Integrate structured and unstructured data from multiple sources into centralized platforms. • Perform feature engineering and optimize data workflows. MLOps & Deployment • Deploy AI/ML models into production securely and efficiently. • Build automated CI/CD pipelines for model training, testing, deployment, and monitoring. • Manage end-to-end AI model lifecycle processes. Monitoring & Optimization • Monitor deployed models for: • Prediction accuracy • Latency • Resource utilization • Reliability • Troubleshoot and optimize production AI systems. Infrastructure & Cloud Management • Manage AI infrastructure using Azure cloud technologies. • Work with containerization and orchestration tools such as Docker and Kubernetes. Responsible AI & Governance • Ensure AI systems are secure, compliant, transparent, explainable, and unbiased. • Implement governance, versioning, monitoring, and rollback strategies. Collaboration & Documentation • Work closely with Data Scientists, DevOps Engineers, Software Engineers, and Business Teams. • Maintain detailed technical documentation throughout the AI/ML lifecycle. Preferred Technical Stack • Azure AI / Azure ML • Python • SQL • Docker • Kubernetes • LangChain / LLM orchestration frameworks • Vector Databases • CI/CD & MLOps tools • Prompt Engineering • RAG Frameworks • GenAI Platforms