226 to 250 of 272 MLOps Jobs in London

Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI/CD and model monitoring to cost and performance optimisation. Partnering with Data Engineers to ensure the pipelines and data structures feeding the models are …/ML or LLM-driven products. Experience with cloud ML platforms (AWS SageMaker, Bedrock, Vertex AI, etc.). Familiarity with LangChain or similar orchestration frameworks. A solid understanding of MLOps and how to take models from concept to production. A collaborative mindset - you enjoy working closely with Product and Data teams and mentoring others when needed. Curiosity, experimentation, and a More ❯

Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI/CD and model monitoring to cost and performance optimisation. Partnering with Data Engineers to ensure the pipelines and data structures feeding the models are …/ML or LLM-driven products. Experience with cloud ML platforms (AWS SageMaker, Bedrock, Vertex AI, etc.). Familiarity with LangChain or similar orchestration frameworks. A solid understanding of MLOps and how to take models from concept to production. A collaborative mindset - you enjoy working closely with Product and Data teams and mentoring others when needed. Curiosity, experimentation, and a More ❯

Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI/CD and model monitoring to cost and performance optimisation. Partnering with Data Engineers to ensure the pipelines and data structures feeding the models are …/ML or LLM-driven products. Experience with cloud ML platforms (AWS SageMaker, Bedrock, Vertex AI, etc.). Familiarity with LangChain or similar orchestration frameworks. A solid understanding of MLOps and how to take models from concept to production. A collaborative mindset - you enjoy working closely with Product and Data teams and mentoring others when needed. Curiosity, experimentation, and a More ❯

Unitary's future. Take ownership of customer outcomes with the autonomy to make decisions that surprise and delight our customers. Contribute full-stack development including software engineering, DevOps, and MLOps, along with light task and project management to ensure your AI solutions deliver maximum value. We are looking for someone who is as excited about Unitary's mission as we … better, but not essential, if you have: Experience working in a fully remote, international team Previous startup experience A background in building and operating agentic AI systems Experience with MLOps practices and tools, and monitoring machine learning systems in production Knowledge of CI/CD practices and tools such as GitLab CI, Argo CD Proficiency with SQL and NoSQL databases More ❯

building data-centric APIs using FastAPI , especially in serverless environments (e.g., Google App Engine) Streaming Data: Familiarity with real-time data pipelines and data ingestion at scale DevOps/MLOps: Exposure to Terraform , Docker , Kubernetes , and MLOps workflows What They Offer A chance to work on real-world data that impacts elite football performance Access to high-value datasets , sports More ❯

building data-centric APIs using FastAPI , especially in serverless environments (e.g., Google App Engine) Streaming Data: Familiarity with real-time data pipelines and data ingestion at scale DevOps/MLOps: Exposure to Terraform , Docker , Kubernetes , and MLOps workflows What They Offer A chance to work on real-world data that impacts elite football performance Access to high-value datasets , sports More ❯

A global lifestyle brand is hiring a Data Science Manager to lead personalisation efforts within its CRM ecosystem. The role sits in the Consumer Intelligence and Experience (CIX) team, which drives customer engagement through predictive analytics and insights across all More ❯

A global lifestyle brand is hiring a Data Science Manager to lead personalisation efforts within its CRM ecosystem. The role sits in the Consumer Intelligence and Experience (CIX) team, which drives customer engagement through predictive analytics and insights across all More ❯

Overview A global lifestyle brand is hiring a Data Science Manager to lead personalisation efforts within its CRM ecosystem. The role sits in the Consumer Intelligence and Experience (CIX) team, which drives customer engagement through predictive analytics and insights across More ❯

Role As our first senior AI hire, you’ll take ownership of designing and building the foundation of our AI ecosystem. You’ll shape the infrastructure strategy, establish our MLOps pipelines, and work closely with product and data teams to enable seamless model development and deployment. Once the environment is established, you’ll play a key role in recruiting and … mentoring two mid-level AI engineers who will join your team. Responsibilities Architect, build, and maintain an in-house AI environment (on-prem or hybrid cloud). Design MLOps workflows for training, deploying, and monitoring models. Integrate and manage containerized AI engines (Docker/Kubernetes). Establish best practices for model versioning, data pipelines, and reproducibility. Collaborate with ML and … or on-premise platforms (non-cloud environments). Solid understanding of data pipelines, APIs, and scalable system architecture. Preferred Experience leading small teams or mentoring other engineers. Familiarity with MLOps tools and best practices. Background in integrating AI solutions into enterprise products. Awareness of privacy, bias mitigation, and model explainability techniques. What We Offer Opportunity to design and own the More ❯

Role As our first senior AI hire, you’ll take ownership of designing and building the foundation of our AI ecosystem. You’ll shape the infrastructure strategy, establish our MLOps pipelines, and work closely with product and data teams to enable seamless model development and deployment. Once the environment is established, you’ll play a key role in recruiting and … mentoring two mid-level AI engineers who will join your team. Responsibilities Architect, build, and maintain an in-house AI environment (on-prem or hybrid cloud). Design MLOps workflows for training, deploying, and monitoring models. Integrate and manage containerized AI engines (Docker/Kubernetes). Establish best practices for model versioning, data pipelines, and reproducibility. Collaborate with ML and … or on-premise platforms (non-cloud environments). Solid understanding of data pipelines, APIs, and scalable system architecture. Preferred Experience leading small teams or mentoring other engineers. Familiarity with MLOps tools and best practices. Background in integrating AI solutions into enterprise products. Awareness of privacy, bias mitigation, and model explainability techniques. What We Offer Opportunity to design and own the More ❯

. Develop pipeline programming using Python, Spark, and SQL; integrate APIs for seamless workflows. Support Machine Learning and AI initiatives, including NLP, Computer Vision, Time Series, and LLMs. Implement MLOps, CI/CD pipelines, data testing, and quality frameworks. Act as an AI super-user, applying prompt engineering and creating AI artifacts. Work independently while providing clear justification for technical … Proficient with cloud platforms (Snowflake, AWS fundamentals). Solid understanding of data architecture, warehousing, and modeling. Programming expertise: Python, Spark, SQL, API integration. Knowledge of ML/AI frameworks, MLOps, and advanced analytics concepts. Experience with CI/CD, data testing frameworks, and versioning strategies. Ability to work effectively in multi-team, vendor-integrated environments. Why This Role Join a More ❯

. Develop pipeline programming using Python, Spark, and SQL; integrate APIs for seamless workflows. Support Machine Learning and AI initiatives, including NLP, Computer Vision, Time Series, and LLMs. Implement MLOps, CI/CD pipelines, data testing, and quality frameworks. Act as an AI super-user, applying prompt engineering and creating AI artifacts. Work independently while providing clear justification for technical … Proficient with cloud platforms (Snowflake, AWS fundamentals). Solid understanding of data architecture, warehousing, and modeling. Programming expertise: Python, Spark, SQL, API integration. Knowledge of ML/AI frameworks, MLOps, and advanced analytics concepts. Experience with CI/CD, data testing frameworks, and versioning strategies. Ability to work effectively in multi-team, vendor-integrated environments. Why This Role Join a More ❯

🔍 ML Ops Engineer – Cloud, Data, and AI Are you passionate about shaping the future of data and cloud technology? We’re partnering with a forward-thinking financial organization investing heavily in its data and AI capabilities — and they’re looking More ❯

🔍 ML Ops Engineer – Cloud, Data, and AI Are you passionate about shaping the future of data and cloud technology? We’re partnering with a forward-thinking financial organization investing heavily in its data and AI capabilities — and they’re looking More ❯

Designation Senior Principal Analyst Function Sales Enablement Experience 5-10 years Location United Kingdom - Edinburgh, London, Manchester Skills AI, Machine Learning, Solution Architecture You'll be part of a collaborative, entrepreneurial environment that values innovation, excellence, and thought leadership. This More ❯

MLOps Engineer Contract London based (hybrid or onsite depending on preference) Outside IR35 £600 to £650 per day I am supporting a client in London who is preparing to scale their machine learning capability. They need an experienced MLOps Engineer who can take models from development into a reliable production environment and keep everything running smoothly throughout the lifecycle. What … stability and performance across the ML platform. Key skills • Strong Python for production environments. • Solid experience with Azure for ML workloads including compute, networking and storage. • Good knowledge of MLOps tools such as MLflow, Azure ML, Kubeflow or similar. • Strong background in CI and CD using GitHub Actions, Azure DevOps or similar. • Experience working in containerised environments using Docker and … have • Experience working on large scale data or ML projects. • Good understanding of data engineering fundamentals. • Experience working in regulated or high availability environments. If you're an experienced MLOps Engineer feel free to apply or send your C.V to nokakpu@revoco-talent.co.uk MLOps Engineer More ❯

MLOps Engineer Contract London based (hybrid or onsite depending on preference) Outside IR35 £600 to £650 per day I am supporting a client in London who is preparing to scale their machine learning capability. They need an experienced MLOps Engineer who can take models from development into a reliable production environment and keep everything running smoothly throughout the lifecycle. What … stability and performance across the ML platform. Key skills • Strong Python for production environments. • Solid experience with Azure for ML workloads including compute, networking and storage. • Good knowledge of MLOps tools such as MLflow, Azure ML, Kubeflow or similar. • Strong background in CI and CD using GitHub Actions, Azure DevOps or similar. • Experience working in containerised environments using Docker and … have • Experience working on large scale data or ML projects. • Good understanding of data engineering fundamentals. • Experience working in regulated or high availability environments. If you're an experienced MLOps Engineer feel free to apply or send your C.V to nokakpu@revoco-talent.co.uk MLOps Engineer More ❯

Solutions Architect - DevOps/DevSecOps/MLOps Solutions Architect UNITED KINGDOM DevOps Presales Engineer - LONDON - UNITED KINGDOM Salary: £160,000 (80/20 split) Remote Status: Hybrid - 3 days/week in LONDON office Job Description: We are looking for a DevOps Presales Engineer for our client, a market leader in DevOps, DevSecOps, and MLOps.This is a company trusted by … at industry events and conferences Train customers and community members on product capabilities Feed frontline insights back to the company to shape the product roadmap Stay ahead of DevOps & MLOps trends and bring that expertise to every customer conversation This is not a back-office DevOps role. You'll be front and centre in the sales process - a trusted technical More ❯

powered solutions . Lead on data strategy , from collection and simulation to training and validation. Ensure AI safety, ethics, explainability , and compliance with defence standards. Define best practices in MLOps , model evaluation, and continuous integration. Essential Skills & Experience MSc (or PhD) in Computer Science, Artificial Intelligence, Machine Learning, Robotics, or related engineering field . 5+ years of experience in AI … or sensor fusion . Excellent communication and problem-solving skills. Desirable Extras Publications in leading AI/ML or robotics conferences (e.g. NeurIPS, CVPR, ICLR, ICRA). Experience with MLOps pipelines, synthetic data, and model optimisation for edge devices . Understanding of sensor fusion, perception pipelines , and AI safety principles . What’s on Offer Highly competitive base salary. Generous More ❯

powered solutions . Lead on data strategy , from collection and simulation to training and validation. Ensure AI safety, ethics, explainability , and compliance with defence standards. Define best practices in MLOps , model evaluation, and continuous integration. Essential Skills & Experience MSc (or PhD) in Computer Science, Artificial Intelligence, Machine Learning, Robotics, or related engineering field . 5+ years of experience in AI … or sensor fusion . Excellent communication and problem-solving skills. Desirable Extras Publications in leading AI/ML or robotics conferences (e.g. NeurIPS, CVPR, ICLR, ICRA). Experience with MLOps pipelines, synthetic data, and model optimisation for edge devices . Understanding of sensor fusion, perception pipelines , and AI safety principles . What’s on Offer Highly competitive base salary. Generous More ❯

Job Title: MLOps Engineer Location: On-site, 5 days per week Total Compensation: Up to £200,000 We are seeking an experienced MLOps Engineer to join a forward-thinking trading organisation. This is an exciting opportunity to design and implement the infrastructure that powers advanced machine learning workflows in a production trading environment. Key Responsibilities: Feature Store & Data Lake: Build … scalable infrastructure for time-series feature storage, retrieval, and versioning optimised for ML workloads. MLOps Pipelines: Design end-to-end workflows for data ingestion, feature engineering, model training, backtesting, and deployment. Data Ingestion Layer: Connect raw data streams into structured, queryable formats (Parquet/Delta Lake). Production Serving: Deploy feature computation and model inference with appropriate latency characteristics. Integration … execution systems to ensure seamless operations. CI/CD Pipeline: Implement and maintain robust continuous integration and deployment pipelines for ML models. Requirements: Strong experience in building and maintaining MLOps pipelines. Hands-on experience with feature stores, data lakes, and time-series data. Proficiency with modern data formats like Parquet and Delta Lake. Familiarity with production ML model deployment and More ❯

Job Title: MLOps Engineer Location: On-site, 5 days per week Total Compensation: Up to £200,000 We are seeking an experienced MLOps Engineer to join a forward-thinking trading organisation. This is an exciting opportunity to design and implement the infrastructure that powers advanced machine learning workflows in a production trading environment. Key Responsibilities: Feature Store & Data Lake: Build … scalable infrastructure for time-series feature storage, retrieval, and versioning optimised for ML workloads. MLOps Pipelines: Design end-to-end workflows for data ingestion, feature engineering, model training, backtesting, and deployment. Data Ingestion Layer: Connect raw data streams into structured, queryable formats (Parquet/Delta Lake). Production Serving: Deploy feature computation and model inference with appropriate latency characteristics. Integration … execution systems to ensure seamless operations. CI/CD Pipeline: Implement and maintain robust continuous integration and deployment pipelines for ML models. Requirements: Strong experience in building and maintaining MLOps pipelines. Hands-on experience with feature stores, data lakes, and time-series data. Proficiency with modern data formats like Parquet and Delta Lake. Familiarity with production ML model deployment and More ❯

🚀 Data Engineer | AI Startup | Real-Time Data & ML Pipelines I’m currently partnered with a next-generation AI startup that’s building real-time intelligence systems capable of understanding and adapting to the world as it happens. They’re at More ❯

🚀 Data Engineer | AI Startup | Real-Time Data & ML Pipelines I’m currently partnered with a next-generation AI startup that’s building real-time intelligence systems capable of understanding and adapting to the world as it happens. They’re at More ❯