Senior AI Engineer

We are looking for a Senior AI Engineer to join our growing Applied AI team. This is ahands-on, technically demanding role for someone who can contribute to building production AI systems while helping raise the technical bar of the team around them. You will work collaboratively across a fast-moving technology company, turning cutting-edge research into practical, scalable solutions.

This role reportsto the AppliedAILead.

• Lead data identification, cleaning, enrichment, preprocessing,feature engineering,andexploratoryanalysis to ensure fitness for AI workflows and to inform modelling and business decisions.
• Build, tune, andoptimisemachine learning, deep learning, and generative AI models,leveraging both established and cutting-edge techniques.
• Designand buildLLM-powered systems— RAG pipelines, prompt and context engineering, fine-tuning, structured outputs, function calling, context-window management,and secure model integration;selecting appropriately between standard and reasoning (test-time-compute) models, balancing capability against latency and cost.
• Designand buildagentic AI systems— tool-calling architectures, interoperability protocols (MCP, agent-to-agent), multi-agent orchestration, and multi-step reasoning with human-in-the-loop and appropriate trust, safety, and security boundaries.
• Leverage and contribute to agentic engineering tooling, including coding assistants,configurable permissions models,internal skills and plugins architectures, and sandboxed autonomous workflows integrated intoCI/CDand delivery pipelines.
• Apply model adaptation techniques including parameter-efficient fine-tuning(LoRA,QLoRA), model distillation, and synthetic data generation for domain-specific and low-resource scenarios.
• Develop evaluation frameworks covering performance, hallucination, safety, cost, and non-deterministicbehaviouracross classical and generative AI systems.
• Optimise model inference and end-to-end pipelines for speed, cost, memory footprint, and scalability, including for CPU-constrained andairgappeddeployment targets.
• Develop andmaintainproduction-grade model pipelines and supporting software with strongMLOpspractices — covering real-time inference, batch processing, performance monitoring, drift detection, scheduled retraining, observability, governance, version control, and reproducibility.
• Providetechnicalguidance— architectural input, peer review, and mentoring of mid-level and junior engineers through code review, pairing, and knowledge sharing — raising the technical bar of the team and shaping secure, scalable, high-performing AI solutions.
• Work within Agile delivery teams — collaborating with data, platform, and software engineers to integrate AI into products, communicating clearly with technical and non-technical stakeholders, and championing engineering excellence and continuous improvement.
• Stay current with the latest research, frameworks, and trends across ML, deep learning, and GenAI, translating them into production-ready solutions.

• 5+ years of commercial experience in AI and machine learning engineering and development — academic research experience is highly valued alongside this, buta track recordof commercial delivery is essential —withdemonstrabledelivery of production-grade models and systems across traditional ML, neural networks, and LLM-based systems.
• 5+years of commercial experience working with Python;familiarity with C# is considered a bonus due to existing product integrations.
• Hold an advanced degree in machine learning, computer science, engineering, or a related discipline, with an MScrequiredand a PhD highly desirable.
• Deep hands-on experience with
  
  PyTorch, Scikit-learn, and the Hugging Face ecosystem, with familiarity withMLFlowand
  
  AzureML; capable of making architecture and implementation decisions.
• Strong competence with Git, pull requests, automated testing, CI/CD,MLOps, and ML pipelines; experience with Azure Dev Ops and cloud-based ML infrastructure (Azure, AWS, or GCP) is beneficial.
• Strong grounding in classical data science fundamentals — feature engineering, statistical analysis, experimental design, and model…