51 to 75 of 370 MLOps Jobs in England

for security clearance. Desirable Skills, but not essential: Proficiency in Arcade scripting across ESRI runtime environments. Familiarity with the Microsoft Azure cloud platform, including data and AI services, and MLOps Knowledge of geospatial data warehousing and analytics. Familiarity with leading AI/ML platforms and libraries Experience building and scheduling ETL workflows. Knowledge of spatial data management and geospatial queries. 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 ❯

Computer Science, Data Science, Engineering, Mathematics, etc.). Strong proficiency in Python Hands-on experience with LLMs , prompt design , RAG architectures , or fine-tuning foundation models . Understanding of MLOps practices (e.g., Docker, FastAPI, MLflow, cloud-based model deployment). Strong grasp of NLP techniques and evaluation methods for generative models. Excellent analytical and communication skills, with a focus on More ❯

Factory, GCP Dataflow and AWS services. Apply data modelling concepts (e.g., medallion architecture) and ensure data quality and governance. Collaborate with DevOps teams to automate CI/CD and MLOps processes using Azure DevOps, Kubernetes, and Terraform. Visualize and communicate insights using Power BI, Tableau, and PowerApps. Mentor junior engineers and contribute to internal knowledge sharing. Ensure solutions meet security 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 ❯

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 ❯

years in a people leadership role. Expertise in AI, machine learning, deep learning, and predictive analytics. Strong proficiency in AI frameworks, cloud AI platforms (AWS, GCP, Azure), and MLOps practices. Solid understanding of AI ethics, governance, and compliance in a corporate setting. Proven track record of leading AI teams and managing AI projects. Strong strategic thinking, problem solving, and data More ❯