AI Engineer

Overview

In this role, you will design, build, and deploy production-grade AI applications and generative AI solutions that integrate with enterprise systems. You’ll work across ML engineering, GenAI, and MLOps to deliver scalable AI capabilities. You’ll collaborate with architects, product managers, and engineering teams to drive reliable AI outcomes. This is a hands-on opportunity to shape AI-enabled products and optimize performance at scale.

Pay / Benefits

• Quarterly Performance-Related Profit Share
• Strong Career Progression & Mentorship
• Flexible/remote working
• Pension
• Private Healthcare Cover
• Life Assurance

Responsibilities

• Design, build, and maintain AI-powered applications and services
• Develop and deploy ML and Generative AI solutions
• Create Retrieval-Augmented Generation (RAG) systems and AI agents
• Integrate foundation models and APIs into enterprise applications
• Create reusable AI components and frameworks
• Train, fine-tune, evaluate, and deploy ML models
• Develop feature engineering and model evaluation pipelines
• Implement model monitoring and performance tracking
• Optimize model inference performance and cost efficiency
• Develop LLM-based applications and workflows
• Build prompt templates, agent frameworks, and orchestration pipelines
• Implement vector search and knowledge retrieval systems
• Design evaluation frameworks for AI quality, safety, and reliability
• Improve hallucination mitigation and response accuracy
• Design and automate ML model training, validation, and retraining pipelines
• Version and manage datasets, features, and model artefacts (MLflow or similar)
• Deploy and serve ML models via REST APIs or batch inference on Databricks/Snowflake
• Monitor model performance in production and detect drift
• Manage feature stores and ensure data pipeline reliability for ML workloads
• Implement experiment tracking and reproducibility across the model lifecycle
• Collaborate with Solution Architects, Product Managers, and Engineering Teams
• Participate in code reviews and technical design discussions
• Support testing, deployment, and ongoing optimisation activities

Key requirements

• Python
• SQL
• Scikit-learn
• PyTorch and/or TensorFlow
• Hugging Face
• Main AI services from leading cloud providers
• Supervised and unsupervised learning
• Model evaluation
• Feature engineering
• Statistical concepts
• RAG systems
• AI agents
• LLM-powered applications
• Vector search solutions
• LangChain
• LlamaIndex
• Semantic Kernel
• LangGraph
• AWS
• Docker
• Kubernetes
• CI/CD pipelines and DevOps practices
• 3+ years in ML engineering or data science
• Production deployment of AI models
• Experience with RAG, LLM integration or agentic workflows
• collaboration
• teamwork
• communication
• Python
• SQL
• Scikit-learn

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