26 to 50 of 274 MLOps Jobs in London

that drive real-world outcomes for complex, high-stakes environments. This is a fast-paced, technically elite environment, ideal for someone who thrives on solving operational challenges, building robust MLOps infrastructure, and leading the delivery of AI systems at scale. The Role As a Senior Machine Learning Engineer, you’ll be part of cross-functional delivery teams working on technically More ❯

that drive real-world outcomes for complex, high-stakes environments. This is a fast-paced, technically elite environment, ideal for someone who thrives on solving operational challenges, building robust MLOps infrastructure, and leading the delivery of AI systems at scale. The Role As a Senior Machine Learning Engineer, you’ll be part of cross-functional delivery teams working on technically More ❯

machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep knowledge of automation tools including GitHub Actions, Terraform, and Ansible Experience with business process automation (RPA) tools like Appian Workflow More ❯

FastAPI. Cloud Platform: Mastery of GCP, particularly Vertex AI, Google Kubernetes Engine (GKE), and Cloud Functions. Databases: Strong command of relational databases like PostgreSQL and familiarity with NoSQL solutions. MLOps & DevOps: Production experience with Docker, Kubernetes, CI/CD pipelines (e.g., Jenkins, GitHub Actions), and Infrastructure as Code (Terraform). Qualifications Bachelor's or Master's degree in Computer Science More ❯

FastAPI. Cloud Platform: Mastery of GCP, particularly Vertex AI, Google Kubernetes Engine (GKE), and Cloud Functions. Databases: Strong command of relational databases like PostgreSQL and familiarity with NoSQL solutions. MLOps & DevOps: Production experience with Docker, Kubernetes, CI/CD pipelines (e.g., Jenkins, GitHub Actions), and Infrastructure as Code (Terraform). Qualifications Bachelor's or Master's degree in Computer Science More ❯

machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance in production Automation Architecture Deep knowledge of automation tools including GitHub Actions, Terraform, and Ansible Experience with business process automation (RPA) tools like Appian Workflow More ❯

Career Growth: Direct mentorship from senior AI engineers and founders, clear progression in a high-growth AI compliance company 🛠 Modern Tech Stack on AWS: Work with cutting-edge LLMs, MLOps tooling, Python, TypeScript, and an AWS-native cloud stack 🤝 Collaborative Culture: Join a supportive, mission-driven team passionate about safe and responsible AI ✨ Additional Perks: Pension scheme, private healthcare, regular More ❯

Career Growth: Direct mentorship from senior AI engineers and founders, clear progression in a high-growth AI compliance company 🛠 Modern Tech Stack on AWS: Work with cutting-edge LLMs, MLOps tooling, Python, TypeScript, and an AWS-native cloud stack 🤝 Collaborative Culture: Join a supportive, mission-driven team passionate about safe and responsible AI ✨ Additional Perks: Pension scheme, private healthcare, regular More ❯

Claude), including prompt engineering, fine-tuning, and evaluation. Hands-on experience with core GenAI frameworks (e.g., LangChain, LlamaIndex) and Agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph). Proficiency in MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Strong working knowledge and practical deployment experience on major cloud platforms (AWS, Azure, GCP), including their AI/ML services. Strong foundation More ❯

Claude), including prompt engineering, fine-tuning, and evaluation. Hands-on experience with core GenAI frameworks (e.g., LangChain, LlamaIndex) and Agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph). Proficiency in MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Strong working knowledge and practical deployment experience on major cloud platforms (AWS, Azure, GCP), including their AI/ML services. Strong foundation More ❯

and analyse large, complex datasets. Strong understanding of data ethics, governance, and responsible AI principles. Must have SC Clearance and 5 years' continuous UK residency . Desirable: Experience with MLOps or deploying ML models into production. Familiarity with data engineering workflows and pipelines. Knowledge of working in a secure or public sector environment. If you believe your experience is in More ❯

and analyse large, complex datasets. Strong understanding of data ethics, governance, and responsible AI principles. Must have SC Clearance and 5 years' continuous UK residency . Desirable: Experience with MLOps or deploying ML models into production. Familiarity with data engineering workflows and pipelines. Knowledge of working in a secure or public sector environment. If you believe your experience is in More ❯

based data platforms (AWS, Azure, or GCP). Proven track record in credit risk modelling, fraud analytics, or similar financial domains. Familiarity with big data technologies (Spark, Hive) and MLOps practices for production-scale deployments. Excellent communication skills to engage stakeholders and simplify complex concepts. Desirable Extras Experience with regulatory frameworks (e.g., Basel, GDPR) and model explainability tools. Knowledge of More ❯

based data platforms (AWS, Azure, or GCP). Proven track record in credit risk modelling, fraud analytics, or similar financial domains. Familiarity with big data technologies (Spark, Hive) and MLOps practices for production-scale deployments. Excellent communication skills to engage stakeholders and simplify complex concepts. Desirable Extras Experience with regulatory frameworks (e.g., Basel, GDPR) and model explainability tools. Knowledge of More ❯

PhD in a numerate discipline (Computer Science, Mathematics, etc.) Strong Python coding skills for production systems Proven experience deploying models in real-world environments (API, batch, streaming) Familiarity with MLOps best practices and containerised deployments (Docker, Kubernetes) Experience working in cloud environments (AWS preferred; Terraform a bonus) Strong communication skills and interest in the latest ML/AI developments Comfortable More ❯

PhD in a numerate discipline (Computer Science, Mathematics, etc.) Strong Python coding skills for production systems Proven experience deploying models in real-world environments (API, batch, streaming) Familiarity with MLOps best practices and containerised deployments (Docker, Kubernetes) Experience working in cloud environments (AWS preferred; Terraform a bonus) Strong communication skills and interest in the latest ML/AI developments Comfortable More ❯

TOGAF, Zachman) and cloud-native architecture (preferably Microsoft Azure). Understanding LLMs and Python-based agentic frameworks like LangChain or AutoGen. Strong understanding of data governance, security protocols, and MLOps practices in a cloud environment. Proven track record of designing and deploying scalable AI solutions that deliver measurable business value. Excellent communication, leadership, and analytical skills, with the ability to More ❯

that drives engagement, conversion, and growth. Oversee the design, training, and deployment of search and recommendation models — from data strategy to monitoring and performance optimisation. Collaborate with platform and MLOps teams to ensure robust, efficient, and scalable ML workflows (including CI/CD, feature management, and monitoring). Share insights and best practices across other ML teams, particularly in areas More ❯

that drives engagement, conversion, and growth. Oversee the design, training, and deployment of search and recommendation models — from data strategy to monitoring and performance optimisation. Collaborate with platform and MLOps teams to ensure robust, efficient, and scalable ML workflows (including CI/CD, feature management, and monitoring). Share insights and best practices across other ML teams, particularly in areas More ❯

that drives engagement, conversion, and growth. Oversee the design, training, and deployment of search and recommendation models — from data strategy to monitoring and performance optimisation. Collaborate with platform and MLOps teams to ensure robust, efficient, and scalable ML workflows (including CI/CD, feature management, and monitoring). Share insights and best practices across other ML teams, particularly in areas More ❯

that drives engagement, conversion, and growth. Oversee the design, training, and deployment of search and recommendation models — from data strategy to monitoring and performance optimisation. Collaborate with platform and MLOps teams to ensure robust, efficient, and scalable ML workflows (including CI/CD, feature management, and monitoring). Share insights and best practices across other ML teams, particularly in areas More ❯

that drives engagement, conversion, and growth. Oversee the design, training, and deployment of search and recommendation models — from data strategy to monitoring and performance optimisation. Collaborate with platform and MLOps teams to ensure robust, efficient, and scalable ML workflows (including CI/CD, feature management, and monitoring). Share insights and best practices across other ML teams, particularly in areas More ❯

that drives engagement, conversion, and growth. Oversee the design, training, and deployment of search and recommendation models — from data strategy to monitoring and performance optimisation. Collaborate with platform and MLOps teams to ensure robust, efficient, and scalable ML workflows (including CI/CD, feature management, and monitoring). Share insights and best practices across other ML teams, particularly in areas 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 ❯