Machine Learning Jobs

• London
• Hybrid
• £75,000 – £95,000 pa
• Senior

Own and develop the MLOps infrastructure behind a growing AI platform, building orchestration pipelines with Dagster, Airflow or Prefect and deploying ML workloads on Kubernetes and cloud. You will implement CI/CD, observability and monitoring, manage infrastructure as code with Terraform or Bicep, and work closely with Data Scientists and ML Engineers to scale production ML, NLP and AI systems.

Dagster · Airflow · Prefect +3

• Huddersfield
• Hybrid
• £35,000 – £45,000 pa
• Junior

Supports AWS infrastructure, CI/CD pipelines and automation for software and machine learning workloads, including packaging, versioning and deploying ML models. Works with software and AI engineers to keep development, testing and production environments consistent, monitoring pipelines and fixing issues. Suits an early-career engineer with cloud, scripting and Git foundations.

AWS · GitHub · GitHub Actions +5

• London
• Senior

Serve as the trusted technical ML and AI expert for enterprise customers, architecting production-grade ML and AI workloads on the Databricks Data Intelligence Platform. Specialise in GenAI solutions such as RAG architectures, agentic systems and AI observability, while mentoring colleagues and influencing the platform's AI roadmap.

Databricks · AWS · Azure +7

• London
• Hybrid
• £80,000 – £110,000 pa
• Senior

Design, build and deploy production-grade machine learning and generative AI solutions across the full ML lifecycle, from experimentation to deployment, monitoring and optimisation. Establish robust MLOps practices, build APIs and improve Python workload and infrastructure performance, working closely with engineering and AI teams in a hybrid London setup.

• London
• Hybrid
• £600 – £650 pd
• Mid-level
• Contract

Design, build and deploy machine learning models into production, with a focus on recommendation systems covering ranking and search optimisation. Work alongside data scientists, engineers and product stakeholders, maintaining MLOps pipelines and ensuring scalability of ML systems on Azure. Six-month contract within a hybrid North London team.

Azure · TensorFlow · PyTorch

• Manchester
• Senior

Lead the design and delivery of AI, machine learning, agentic AI and RAG-based solutions for a financial consultancy's client engagements. Build production-grade models with Python and PyTorch, design MLOps frameworks, CI/CD pipelines and cloud-native model serving, while mentoring engineers and shaping technical strategy.

PyTorch · Google Cloud Platform · GCP +5

• London
• Senior
• Contract

Builds ML and graph-based identity resolution where rule-based matching falls short, improving customer-spine architecture and reducing identity drift. Defines matching-quality metrics, supports golden-layer integration and delivers a reusable identity-resolution framework. Requires entity resolution, fuzzy matching, MLOps and feature-store skills plus Customer Master/MDM experience.

MDM

• Dublin, Ireland
• Hybrid · 2 days in office
• Lead

Lead the architecture, deployment and operation of a large-scale AI/ML platform spanning conventional ML and agentic AI. Own the platform roadmap, MLOps/LLMOps practices, CI/CD and IaC standards, and Azure-based infrastructure, while mentoring engineers and acting as senior escalation point. Hands-on leadership role combining coding with technical strategy and architecture decisions.

Azure · AWS · Terraform +4

• London
• Hybrid
• Senior

Develops production-grade ML models and pipelines for physics simulation, working with research scientists and simulation engineers. Involves scaling training and inference across multi-GPU/multi-node distributed systems, optimising from job scheduling down to CUDA/Triton/Warp kernels, and building cloud infrastructure on CoreWeave and AWS.

AWS · Azure · GCP +7

• United Kingdom
• Remote
• Senior
• Permanent · Flexible

Customer-facing AI solutions architect role supporting government clients across the UK and Europe, educating prospects, running proofs of concept, productising workflows and guiding deployments through partner ecosystems. Day to day involves hands-on work with Python, PyTorch, Kubernetes and agentic/GenAI solutions, plus mentoring partners and shaping national and regional AI strategy. Travel, some international, may be required.

NVIDIA · PyTorch · Kubernetes +3

• London
• Hybrid
• Senior

Customer-facing engineering role building and productionizing data and AI solutions on the Databricks platform. You own architecture and design decisions, delivering end-to-end systems spanning data engineering, ML/AI model integration and user-facing applications, embedding with client teams and travelling to customers around 20% of the time.

Databricks · AWS · Azure +7

• Singapore
• Hybrid
• Senior

Build physical foundation models for industrial engineering, scaling training across multi-GPU cloud compute and turning research prototypes into robust, optimised implementations. Work at the intersection of data science and software engineering, owning research work-streams and translating results into reusable libraries, tooling and products alongside scientists, customers and partners.

PyTorch · JAX · NumPy +7

• London
• Hybrid · 2–3 days in office

Owns the infrastructure behind speech AI models, building scalable data pipelines for ingesting, transforming and validating large training datasets, plus web scraping and data acquisition tooling. Also operates the platform for training, evaluating and serving models, optimising GPU utilisation, scheduling and observability across distributed systems.

Docker · Kubernetes

• Cambridge
• Hybrid · 2–3 days in office

Own the infrastructure behind speech AI models, building scalable data pipelines for ingesting, transforming and validating large training datasets, plus web scraping and data acquisition. Operate the platform that trains, evaluates and serves models in production, optimising GPU utilisation, scheduling and observability across distributed systems.

Docker · Kubernetes

• London
• Hybrid
• Senior

Owns the software engineering that brings data science and ML products into live production for the Autoplanner team, building AI-powered planning systems for global warehouses and last-mile assets. Works mainly in typed, well-tested Python (with some Java for OSP integration), handling cloud deployments, CI/CD, APIs, and MLOps model serving and monitoring on AWS.

AWS · GCP · Python +1