AI Engineer

In this role you will build, deploy, and optimize production-grade AI applications and Generative AI solutions. You work closely with cross-functional teams to integrate AI models into enterprise systems, delivering reliable AI at scale. You will shape AI workflows, from model training to deployment, with a focus on performance, safety, and value for customers. This opportunity combines hands-on engineering with collaboration across Solution Architects, Product Managers, and Engineering Teams in a growth-focused technology partner environment.

• Quarterly Profit Share Scheme
• flexible/remote working
• Pension
• Private Healthcare
• Life Assurance
• Educational assistance

• Design, build, and maintain AI-powered applications and services
• Develop and deploy ML and Generative AI solutions
• Build 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 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 training, validation, and retraining pipelines
• Version and manage datasets, features, and model artefacts using MLflow or similar
• Deploy and serve ML models via REST APIs or batch inference at scale on Databricks, Snowflake or similar
• Monitor model performance in production, detect drift and degradation
• 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

• Python
• SQL
• Scikit-learn
• PyTorch and/or TensorFlow
• Hugging Face
• Main AI services from leading cloud service and model 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 and containerization
• Kubernetes (preferred)
• CI/CD pipelines and DevOps practices
• 3+ years in ML engineering or data science with production deployments
• Experience deploying models to production and building Gen AI applications including RAG systems, LLM integration, or agentic workflows

• collaboration with cross-functional teams
• code reviews and technical design discussions
• proactive problem solving
• Python
• SQL
• Scikit-learn