76 to 100 of 370 MLOps Jobs in England

MLOps Engineer Outside IR35 - 500-600 Per Day Ideally, 1 day per week/fortnight in the office, flexibility for remote work for the right candidate. A market-leading global e-commerce client is urgently seeking a Senior MLOps Lead to establish and drive operational excellence within their largest, most established data function (60+ engineers). This is a mission … critical role focused on scaling their core on-site advertising platform from daily batch processing to real-time capability. This role suits a hands-on MLOps expert who is capable of implementing new standards, automating deployment lifecycles, and mentoring a large engineering team on best practices. What you'll be doing: MLOps Strategy & Implementation: Design and deploy end-to-end … MLOps processes, focusing heavily on governance, reproducibility, and automation. Real-Time Pipeline Build: Architect and implement solutions to transition high-volume model serving (10M+ customers, 1.2M+ product variants) to real-time performance. MLflow & Databricks Mastery: Lead the optimal integration and use of MLflow for model registry, experiment tracking, and deployment within the Databricks platform. DevOps for ML: Build and automate More ❯

Qualifications • 6–10 years of experience in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g. More ❯

Qualifications • 6–10 years of experience in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g. More ❯

Qualifications: • 6+ years of experience in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit More ❯

delivering AI/ML or automation projects end-to-end Strong skills in Python and modern ML/NLP/LLM techniques Experience with cloud, CI/CD, and MLOps workflows Ability to explain complex concepts clearly to varied audiences Experience with Databricks, Azure or AWS preferable More ❯

clean code, scalability, and technical excellence Nice to Have Experience with RAG systems, multi-agent AI, or Langfuse/LiteLLM Python proficiency for AI/ML integration Familiarity with MLOps and cost optimisation for AI systems Background in B2B SaaS or financial software More ❯

etc.) ☁️ Familiarity with cloud platforms (GCP preferred, or AWS/Azure) 🔍 Strong analytical mindset and ability to solve complex problems 🤝 Excellent communication and collaboration skills ⚙️ Experience with data engineering, MLOps, or automation tools is a plus Why Join Us? 🌍 Work with global teams across Europe 📈 Huge opportunity to grow in a major digital transformation environment 🤖 Build cutting-edge AI solutions More ❯

etc.) ☁️ Familiarity with cloud platforms (GCP preferred, or AWS/Azure) 🔍 Strong analytical mindset and ability to solve complex problems 🤝 Excellent communication and collaboration skills ⚙️ Experience with data engineering, MLOps, or automation tools is a plus Why Join Us? 🌍 Work with global teams across Europe 📈 Huge opportunity to grow in a major digital transformation environment 🤖 Build cutting-edge AI solutions More ❯

agile delivery approach, consistently documenting designs and results for leadership. Experience and Expertise Required We are looking for hands-on experience in the following core areas: Model Lifecycle Management (MLOps): Proven experience in implementing robust AI/ML pipelines for model training, validation, and deployment (e.g., using MLflow, Vertex AI, or Azure ML). Expertise in managing model evaluation, drift More ❯

agile delivery approach, consistently documenting designs and results for leadership. Experience and Expertise Required We are looking for hands-on experience in the following core areas: Model Lifecycle Management (MLOps): Proven experience in implementing robust AI/ML pipelines for model training, validation, and deployment (e.g., using MLflow, Vertex AI, or Azure ML). Expertise in managing model evaluation, drift More ❯

high-growth AI-driven fintech 🧠 Deep Technical Work: Opportunity to work on challenging, high-impact AI problems with real financial data and users 🛠 Modern Engineering Practices: Exposure to modern MLOps, experimentation workflows, and best practices in production AI 🤝 Collaborative Culture: Join a supportive, intellectually rigorous team that values deep thinking, ownership, and high-quality engineering ✨ Additional Perks: Pension scheme, private More ❯

high-growth AI-driven fintech 🧠 Deep Technical Work: Opportunity to work on challenging, high-impact AI problems with real financial data and users 🛠 Modern Engineering Practices: Exposure to modern MLOps, experimentation workflows, and best practices in production AI 🤝 Collaborative Culture: Join a supportive, intellectually rigorous team that values deep thinking, ownership, and high-quality engineering ✨ Additional Perks: Pension scheme, private More ❯

MLOps Engineer Outside IR35 - 500-600 Per Day Ideally, 1 day per week/fortnight in the office, flexibility for remote work for the right candidate. A market-leading global e-commerce client is urgently seeking a Senior MLOps Lead to establish and drive operational excellence within their largest, most established data function (60+ engineers). This is a mission … critical role focused on scaling their core on-site advertising platform from daily batch processing to real-time capability. This role suits a hands-on MLOps expert who is capable of implementing new standards, automating deployment lifecycles, and mentoring a large engineering team on best practices. What you'll be doing: MLOps Strategy & Implementation: Design and deploy end-to-end … MLOps processes, focusing heavily on governance, reproducibility, and automation. Real-Time Pipeline Build: Architect and implement solutions to transition high-volume model serving (10M+ customers, 1.2M+ product variants) to real-time performance. MLflow & Databricks Mastery: Lead the optimal integration and use of MLflow for model registry, experiment tracking, and deployment within the Databricks platform. DevOps for ML: Build and automate More ❯

experience developing ML models, with a strong focus on medical imaging Proven track record using deep learning frameworks (PyTorch/TensorFlow) Solid Python skills and familiarity with cloud or MLOps tools (Docker, Kubernetes, MLFlow) Understanding of data pipelines and image analysis techniques (segmentation, registration, feature extraction) Benefits Share options and private healthcare Flexible hybrid working 28 days’ holiday + bank More ❯

Excellent communication and cross-functional collaboration skills Nice to have Experience with vector databases , RAG systems , or multi-agent AI Python skills for AI/ML development Familiarity with MLOps , Langfuse , or LiteLLM Background in fintech , SaaS , or payments software Join a forward-thinking fintech where you’ll have the freedom to innovate, influence technical direction, and build AI systems More ❯

technical delivery. Desirable Skills Background in full-stack or backend engineering, using technologies such as Python, TypeScript, or .NET. Experience with data engineering and data architecture concepts. Familiarity with MLOps practices and AI development frameworks (e.g., Azure AI, LangChain, Hugging Face). Relevant certifications in Azure Architecture, Data, or AI disciplines. Knowledge of automation tools, monitoring, and observability platforms. If More ❯

Job Title: ML Engineer (AI) Location: London (Hybrid – 3 days in office) Industry: Media, Campaign Media, AI/Data Tech: Python, Data engineering, ML pipelines, MLOps, Model deployment Salary: £60-75k + shares *Unfortunately, Visa sponsorship is not on offer for this position. About the Role We’re hiring an AI Data Engineer to help build the next generation More ❯

Job Title: ML Engineer (AI) Location: London (Hybrid – 3 days in office) Industry: Media, Campaign Media, AI/Data Tech: Python, Data engineering, ML pipelines, MLOps, Model deployment Salary: £60-75k + shares *Unfortunately, Visa sponsorship is not on offer for this position. About the Role We’re hiring an AI Data Engineer to help build the next generation More ❯

Experience integrating AI models into production systems using GCP, AWS, or Azure. Familiarity with vector databases, embedding models, or retrieval-augmented generation (RAG). Knowledge of Docker, Airflow, or MLOps pipelines. Strong understanding of AI ethics, data privacy, and responsible model deployment. TO BE CONSIDERED... Please either apply online or email me directly at .By applying for this role, you More ❯

stacks (TypeScript, Node.js, Go, or similar) Strong understanding of ML principles and model evaluation Experience with cloud-based model deployment (GCP preferred) Familiarity with containerised workflows (Docker, serverless) and MLOps tools (MLflow, Vertex AI, SageMaker) Excellent communication and collaboration skills in a remote-first team Willingness to travel occasionally for in-person collaboration or client work Nice to Have Experience More ❯

stacks (TypeScript, Node.js, Go, or similar) Strong understanding of ML principles and model evaluation Experience with cloud-based model deployment (GCP preferred) Familiarity with containerised workflows (Docker, serverless) and MLOps tools (MLflow, Vertex AI, SageMaker) Excellent communication and collaboration skills in a remote-first team Willingness to travel occasionally for in-person collaboration or client work Nice to Have Experience More ❯

Demonstrated experience working with large-scale health, genomic, or biobank datasets (e.g. UK Biobank, All of Us, Our Future Health). Exposure to production deployment and model lifecycle management (MLOps awareness a plus). Strong communicator with the ability to operate between science and engineering teams. Nice to Have Experience integrating multi-omic or imaging data with clinical outcomes. Knowledge More ❯

Demonstrated experience working with large-scale health, genomic, or biobank datasets (e.g. UK Biobank, All of Us, Our Future Health). Exposure to production deployment and model lifecycle management (MLOps awareness a plus). Strong communicator with the ability to operate between science and engineering teams. Nice to Have Experience integrating multi-omic or imaging data with clinical outcomes. Knowledge More ❯

performance and manage end-to-end ML lifecycle (data ingestion, training, evaluation, deployment). Mentor junior engineers and contribute to code reviews, best practices, and technical decision-making. Implement MLOps pipelines for continuous training, deployment, and monitoring of models. Ensure compliance with data privacy and security regulations (GDPR, etc.). Required Skills & Qualifications: Bachelor's or Master's degree in … Solid understanding of ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on experience with MLOps tools (MLflow, SageMaker, Kubeflow, etc.) and version control systems. Strong knowledge of APIs, microservices architecture, and CI/CD pipelines. Proven experience in leading teams, managing stakeholders, and delivering end More ❯

Communicate complex concepts to all stakeholders. At least 3 years of experience in AI solution engineering. Large Language Models experience including prompt engineering and RAG implementations. Expert data analytics, MLOps practices and API development. Desirable knowledge in Docker and Kubernetes More ❯