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