Machine Learning Engineer

Job Title: ML Engineer – GenAI / LLM / Azure

Location: London, Canary Wharf, UK

Work Mode: Hybrid – 3 Days Onsite per Week

Contract Duration: 12 Months

• Overview:

We are seeking an experienced ML Engineer with strong expertise in Azure, Generative AI, and Large Language Models (LLMs) to join a high-performing AI engineering team delivering enterprise-scale intelligent solutions.

The ideal candidate will have hands-on experience in designing, deploying, and optimizing AI/ML systems, with particular focus on GenAI applications, RAG architectures, model lifecycle management, and scalable MLOps practices.

Key Responsibilities:

• Design, develop, and deploy scalable AI/ML solutions using Azure cloud technologies

• Build and optimize LLM-based applications and Generative AI solutions

• Develop Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and enterprise data sources

• Fine-tune pretrained LLMs using PEFT methodologies including LoRA and QLoRA

• Design and maintain robust ETL/ELT data pipelines for AI model training and inference

• Implement AI model monitoring, performance tuning, versioning, and lifecycle management

• Build and manage automated CI/CD pipelines for model deployment and retraining workflows

• Collaborate closely with Data Scientists, DevOps Engineers, and business stakeholders during the end-to-end model development lifecycle

• Deploy containerized AI applications using Docker and Kubernetes

• Ensure AI solutions comply with Responsible AI principles including fairness, transparency, governance, and security standards

• Support infrastructure provisioning and optimization across cloud-based AI environments

• Maintain technical documentation and contribute to best practices for scalable AI engineering

Required Skills and Experience:

• 5+ years of experience in Machine Learning Engineering or AI Engineering

• Strong hands-on experience with Microsoft Azure

• Proven experience working with Large Language Models (LLMs) and Generative AI solutions

• Experience building and deploying RAG architectures

• Expertise in MLOps, CI/CD pipelines, and model deployment strategies

• Experience with Docker and Kubernetes

• Strong Python programming skills

• Experience with model monitoring, observability, and performance optimization

• Familiarity with vector databases and embedding workflows

• Strong understanding of AI governance and Responsible AI practices

Nice to Have:

• Experience within the Insurance domain

• Exposure to Agentic AI systems and autonomous AI workflows