176 to 200 of 301 MLOps Jobs in England

build, testing, deployment, monitoring and handover. This is a senior, hands-on delivery role for someone who can work independently across architecture, integration, DevOps, MLOps, AIOps, data engineering and governance. You will make technical decisions, solve complex integration challenges and ensure AI solutions are reliable, scalable and safe … Landing Zone sandbox to test technical options, demonstrate value and provide clear go/no-go recommendations before full delivery. DevOps, MLOps and AIOps ownership: Implement and maintain CI/CD, monitor and release pipelines using Azure DevOps, including version control, rollback capability, automated testing and production health monitoring. Documentation ...

supporting organisations as they move from strategy and experimentation through to enterprise deployment. You will work closely with AI Engineers, Data Scientists, Enterprise Architects, MLOps teams, business stakeholders and senior leadership to design, deliver and scale AI solutions that drive measurable business outcomes. Key responsibilities: Translating business objectives into … prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience designing APIs and integrating AI solutions into enterprise environments Strong understanding of modern data architectures and platform design ...

supporting organisations as they move from strategy and experimentation through to enterprise deployment. You will work closely with AI Engineers, Data Scientists, Enterprise Architects, MLOps teams, business stakeholders and senior leadership to design, deliver and scale AI solutions that drive measurable business outcomes. Key responsibilities: Translating business objectives into … prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience designing APIs and integrating AI solutions into enterprise environments Strong understanding of modern data architectures and platform design ...

long‐term objectives. The role combines strong technical leadership with accountability for scalable, reliable outcomes across squads, leveraging Azure AI services and modern MLOps practices. #J-18808-Ljbffr ...

robots and scale data processing across petabyte-scale datasets. You will define architecture, make key decisions, and work closely with ML researchers to improve MLOps and evaluation workflows while delivering transparent data dashboards for stakeholders. #J-18808-Ljbffr ...

Engineering to lead the design, deployment, and governance of enterprise AI capabilities. You will oversee ML platforms, generative AI, LLM services, feature pipelines and MLOps tooling to deliver measurable business value. The role requires significant leadership in AI engineering, deep ML and cloud experience, and a track record of driving ...

Learning Engineer in London to own the deployment and production readiness of ML models. You will build scalable inference pipelines, manage costs, and drive MLOps along with a cross‐functional team. You’ll work on imaging and text data pipelines, real‐time LLM reporting, and established models, partnering with product ...

Conversational AI pilots, transforming prototypes into production solutions and driving customer value on site at major clients. You will require deep software engineering, MLOps, and cloud infrastructure expertise at Google Cloud. You will design scalable AI systems on GCP, implement robust security and observability, and collaborate with Enterprise GTM teams ...

deploy ML services while ensuring compliance with industry regulations. Ideal candidates have extensive experience in ML engineering with knowledge of NLP, Computer Vision, and MLOps tools. The company values diversity and offers equal opportunities for all candidates. #J-18808-Ljbffr ...

data science and machine learning, including model development, training, evaluation, and deployment. Experience building and deploying chatbots or conversational AI systems. Knowledge of MLOps tools and pipelines for model versioning and deployment. If you are available and interested in a 6-month Outside IR35 contract, please apply in the first ...

across the organisation. You will be responsible for translating complex analytical outputs into clear, commercially focused recommendations, helping establish best practices across experimentation, modelling, MLOps, and data science delivery, and supporting the long‐term evolution of the organisation’s AI and data science capability. This is a leadership role with ...

will be responsible for translating complex customer and behavioural data into clear, actionable recommendations. You'll establish best practices across experimentation, customer modelling, MLOps, and data science delivery, while supporting the continued growth of the organisation's customer analytics and AI capabilities. Skills and Experience Previous experience leading or managing ...

world‐class talent to solve the most complex challenges in high‐DOF autonomous systems and embodied AI. Expertise areas AI/Machine Learning MLOps Software Engineering Data Science The environment The mission is driven by high‐bandwidth, in‐person collaboration. This is a 5‐day‐a‐week, onsite role ...

create real commercial leverage Skills and Experience Proven leadership in defining and delivering enterprise AI strategy, including GenAI Strong understanding of AI platforms, MLOps, and modern AI tooling (without needing to be hands‐on) Experience translating AI opportunities into commercially impactful use cases at scale Exceptional communication, influence, and stakeholder ...

relooking for someone with extensive experience designing, building, and deploying production-grade AI andMLsolutions,supported bya strong understanding of ML engineering,Machine Learning Operations(MLOps), and model lifecyclemanagement.You’llbring the technicalexpertiseto translate analytical models and decision strategies into robust, operational systems that deliver business value within regulated environments. To succeed ...

relooking for someone with extensive experience designing, building, and deploying production-grade AI andMLsolutions,supported bya strong understanding of ML engineering,Machine Learning Operations(MLOps), and model lifecyclemanagement.You’llbring the technicalexpertiseto translate analytical models and decision strategies into robust, operational systems that deliver business value within regulated environments. To succeed ...

relooking for someone with extensive experience designing, building, and deploying production-grade AI andMLsolutions,supported bya strong understanding of ML engineering,Machine Learning Operations(MLOps), and model lifecyclemanagement.You’llbring the technicalexpertiseto translate analytical models and decision strategies into robust, operational systems that deliver business value within regulated environments. To succeed ...

experiences, and helping define the next generation customer data and marketing technology ecosystem. Equally important will be establishing how Data Science, AI Engineering and MLOps functions operate together in an AI-enabled business, whilst defining the long-term operating model for Consumer Data Science as the organisation continues its transformation. ...

data architecture decisions across portfolios. Data, AI Platforms & Technical Leadership Define and evolve enterprise data architecture and AI platform strategy (data pipelines, MLOps, model lifecycle management). Drive adoption of cloud‐native, scalable AI architectures and modern data ecosystems. Lead the integration and architectural design of AI capabilities within core ...

internal and selected external technical forums where appropriate, contributing to knowledge sharing and continuous improvement. Operations, Maintenance, Support and Documentation Leads the implementation of MLOps practices within their area, supporting efficient model deployment, monitoring and lifecycle management. Ensures that CI/CD practices are applied to AI/ML workflows … Demonstrates strong practical experience with Azure‐based AI services, including Azure OpenAI, AI Search and AI Studio. Well‐versed in modern machine learning approaches, MLOps practices and cloud‐based AI architectures. Proficient in Python, common machine learning frameworks, distributed processing concepts and core MLOps practices. Experience designing APIs, building microservices ...

solutions from development through to deployment and monitoring * Building scalable Generative AI and Agentic AI solutions, including RAG pipelines and orchestration frameworks * Implementing MLOps frameworks, governance and best practices * Translating business requirements into cloud-native AI and data architectures across Azure, AWS and GCP * Collaborating with technical and non-technical … stakeholders to deliver measurable business value * Supporting innovation initiatives, reusable assets, thought leadership and client proposals Candidates should have strong experience in AI Engineering, MLOps and cloud-native AI platforms, alongside hands-on knowledge of technologies such as LangChain, LlamaIndex, MLflow, Kubernetes, Docker and CI/CD tooling. Applicants will ...

supporting organisations as they move from strategy and experimentation through to enterprise deployment. You will work closely with AI Engineers, Data Scientists, Enterprise Architects, MLOps teams, business stakeholders and senior leadership to design, deliver and scale AI solutions that drive measurable business outcomes. Key responsibilities Translating business objectives into … prompt engineering, embeddings, semantic search and RAG patterns Exposure to vector databases and agent frameworks such as LangChain, LangGraph or similar technologies Understanding of MLOps, LLMOps and model lifecycle management principles Experience designing APIs and integrating AI solutions into enterprise environments Strong understanding of modern data architectures and platform design ...

applications on the Databricks Data Intelligence Platform. You will also continue to sharpen your technical expertise in cutting‐edge areas like GenAI, ML, MLOps, and LLMOps, while mentoring colleagues and establishing yourself as an AI thought leader. Impact you will have Architecting Workloads: Design and implement production‐level … workloads, including end‐to‐end pipelines, training/inference optimization, MLOps lifecycle management, and integration with cloud‐native services. GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool‐calling, multi‐agent orchestration, and guardrails), AI observability, and natural language querying ...

applications on the Databricks Data Intelligence Platform. You will also continue to sharpen your technical expertise in cutting‐edge areas like GenAI, ML, MLOps, and LLMOps, while mentoring colleagues and establishing yourself as an AI thought leader. Impact you will have Architecting Workloads: Design and implement production-level … workloads, including end-to-end pipelines, training/inference optimization, MLOps lifecycle management, and integration with cloud-native services. GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool‐calling, multi‐agent orchestration, and guardrails), AI observability, and natural language querying ...

solution lifecycle—assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and MLOps/LLMOps pipelines for training and production, and ensuring quality, value, and reliability of deployed systems. THE WORK Formulate real‐world problems into practical, efficient … Develop and implement machine learning algorithms, models, and computational systems; design and build scalable data pipelines to support model training and production with DevOps & MLOps Customize and apply Deep Learning and Generative AI models for various use cases based on the business needs, data availability, system and infrastructure requirements—including ...