Job Title: AI/Machine Learning Engineer
Location: UK
Contract Type: Contractor
Travel: Occasional travel as required

Role Purpose

This role focuses on deploying, managing, and optimising Large Language Models (LLMs) and other machine learning solutions within a cloud-native environment. The successful candidate will build production-ready ML-Ops infrastructure, with a strong emphasis on Databricks, scalable ML workflows, and the effective use of cloud resources.

A critical requirement is deep, hands-on expertise with Databricks, MLOps, and unstructured data processing. The engineer will contribute to the development of AI and data products that drive operational efficiency, enhance decision-making, and support intelligent automation across the organisation.

Key Responsibilities

• Design, build, and deploy machine learning models using Databricks, cloud ML services, and modern LLM tooling.
• Implement, optimise, and scale LLMs for a range of enterprise applications.
• Develop robust MLOps pipelines to support end-to-end model life cycle management, including experimentation, CI/CD, deployment, monitoring, and governance.
• Build workflows for processing and analysing unstructured data (text, documents, audio, image, etc.).
• Collaborate with data scientists, data engineers, platform teams, and product stakeholders to operationalise ML solutions.
• Monitor and troubleshoot production models to ensure performance, reliability, and ongoing optimisation.
• Design and integrate agentic AI workflows and autonomous orchestration solutions using modern LLMOps frameworks.
• Maintain clear documentation of models, workflows, and operational processes.
• Stay current with advancements in LLMs, MLOps, distributed compute, and cloud-native AI tooling.

Skills & Experience

Essential:

• Extensive hands-on experience with Databricks, including model development, data engineering workflows, and ML runtime environments.
• Strong background in MLOps, including MLflow, CI/CD, model registry management, experiment tracking, and scalable deployment strategies.
• Proven experience working with unstructured data and building pipelines to extract, transform, index, and analyse it.
• Strong knowledge of LLMOps practices across deployment, monitoring, optimisation, and governance.
• Proficiency in Python, PyTorch, and modern LLM frameworks (eg, LangChain, LangSmith).
• Experience deploying cloud-native ML systems, including containerisation (Docker) and orchestration (Kubernetes).
• Solid understanding of cloud compliance, governance, and core cloud services (eg, VMs, identity management, automation).
• Experience building ETL/ELT workflows using platforms such as Databricks pipelines or cloud data factory tools.
• Proficiency with Git-based version control and CI/CD pipelines.
• Comfortable working in Agile product teams, participating in stand-ups, sprint planning, and retrospectives.
• Strong communication skills and a collaborative mindset.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML Engineering, or a related field.
• 5+ years of hands-on experience in machine learning engineering and data engineering.
• Demonstrated experience with Databricks and cloud-based ML services.