226 to 250 of 346 MLOps Jobs in the UK

or prototypes at high velocity, ideally in startup or research contexts where speed and adaptability are paramount. Deep expertise in Generative AI, multi-agent systems, LLMs, end-to-end MLOps, and AI infrastructure. Exposure to Deep Learning, Reinforcement Learning, federated learning, AI evaluation or ML fundamentals is highly beneficial. Active interest and awareness of SOTA in multi-agent systems, collective More ❯

vision to build systems that are faster, more thorough and more accurate than humans. Evals: Build benchmarks, evaluate backtests and analyse results to quantify our performance and prevent regressions. MLOps: Ensure that we maintain good practices around collecting and managing datasets, backtests and benchmarks to to maximise development velocity Prompt Engineering: Develop and test prompts to optimise model performance and More ❯

NoSQL) Practical experience in establishing data governance frameworks, data quality initiatives, and Master Data Management solutions Understanding of data requirements for machine learning, including feature stores, data versioning, and MLOps principles Familiarity with common retail data domains (e.g., customer, sales, inventory, product, supply chain, marketing data) Ability to translate complex technical concepts into clear business terms and effectively communicate with More ❯

AI/ML models. The ideal candidate will have deep expertise in cloud technologies (Azure), Infrastructure as Code (IaC), container orchestration, and CI/CD pipelines. An understanding of MLOps and data pipeline tools is also essential for integrating with our AI components. Core Responsibilities Cloud Platform & Infrastructure Management Design, implement, and manage secure and high-performance cloud infrastructure on … end and back-end services. Implement monitoring, logging, and alerting solutions to ensure the health and performance of our platform. Promote a culture of automation across the development lifecycle. MLOps and Data Pipeline Integration Collaborate with data scientists and engineers to support the operationalization of machine learning models. Possess a strong understanding of data pipeline orchestration tools such as Apache … on experience with Kubernetes for container orchestration in a production environment. Experience with building and managing CI/CD pipelines (e.g., Jenkins, GitHub Actions, Azure DevOps). Familiarity with MLOps practices and tools including Apache Airflow is a significant plus. Technical Skills: Proficiency in programming languages such as Bash and Python Strong understanding of version control systems (e.g., Git). More ❯

insights and enhance engagement. Lead the development, evaluation, and monitoring of models (LLMs, predictive/classification, agentic workflows), ensuring security and compliance standards are met. Implement best practices for MLOps, including CI/CD, automated testing, model versioning, and data validation. Architect integrations with vector databases, cloud platforms, and retrieval-augmented generation systems. Qualifications: 3+ years of experience delivering full … Experience with frontend frameworks: React, Vue, or Angular. Hands-on experience building and scaling ML models (LLMs, NLP, classification) and deploying them as APIs/services. Strong skills in MLOps: containerisation (Docker, Kubernetes), cloud deployment (AWS, GCP, Azure), and CI/CD pipelines. Experience with prompt engineering, LLM evaluation, and vector databases (Pinecone, Weaviate, FAISS). Excellent communication skills and More ❯

define AI use cases, and iterate quickly on prototypes. Integrate complementary AI capabilities-such as voice processing, computer vision, and NLP-into customer-facing and internal tools. Collaborate with MLOps and platform engineers to ensure models are deployed, monitored, and iterated in production environments. Maintain clear documentation for AI systems, APIs, and workflows. Stay on top of emerging AI frameworks … using containerised solutions (Docker, Kubernetes) and frameworks like BentoML or equivalent. Familiarity with vector databases and retrieval pipelines for RAG architectures. Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps tooling (MLflow, Kubeflow, or similar). Familiarity with voice-to-text, IVR, and/or computer vision systems is a plus. Strong understanding of software engineering best practices-testing, CI More ❯

and Computer Vision Take ownership of developing, training and productionising machine learning lifecycles, adhering to best practices, security needs and quality assurance Development of neural networks Implementing and maintaining MLops workflows Work alongside Data Engineers and DevOps Engineers to ensure continuous integration and deployment of machine learning models in production. Proactive learning and researching new technologies and software versions Working … Knowledge of machine learning architectures, loss functions, tools and techniques Experience training machine learning models, including hyperparameter tuning and optimizing model performance Experience with (or at least exposure to) MLops workflows Experience with Python Experience with SQL Critical thinking and ability to problem solve Experience with data warehousing and database systems Exposure to working with CI/CD Knowledge of More ❯

experiment, ship, and see your work reflected in customers' financial freedom. At Updraft, you'll help build a fairer credit system. The Role We're looking for an experienced MLOps Engineer for a 3-month contract to lead the development of our ML deployment, testing, monitoring, and feature engineering pipelines . You'll be responsible for establishing best practices and … learning workflows from training to deployment and beyond. The role could be extended to a longer DevOps contract. What You'll Do - Design and build an end-to-end MLOps pipeline using AWS , with a strong focus on SageMaker for training, deployment, and hosting. - Integrate and operationalize MLflow for model versioning, experiment tracking, and reproducibility. - Architect and implement a feature … workflows (training, evaluation, deployment), integrating with tools such as GitHub Actions , CodePipeline , or Jenkins . - Create internal documentation and onboarding guides for engineering and data teams to adopt new MLOps practices. What We're Looking For - 3+ years of experience in MLOps, DevOps, or ML infrastructure roles. - Deep familiarity with AWS services , especially SageMaker , S3, Lambda, CloudWatch, IAM, and optionally More ❯

As Opus 2 continues to embed AI into our platform, we need robust, scalable data systems that power intelligent workflows and support advanced model behaviours. Were looking for an MLOps Engineer to build and maintain the infrastructure that powers our AI systems. You will be the bridge between our data science and engineering teams, ensuring that our machine learning models … is passionate about building robust, scalable, and automated systems for machine learning, particularly for cutting-edge LLM-powered applications. What you'll be doing Design, build, and maintain our MLOps infrastructure, establishing best practices for CI/CD for machine learning, including model testing, versioning, and deployment. Develop and manage scalable and automated pipelines for training, evaluating, and deploying machine … strategy and roadmap by providing expertise on the operational feasibility and scalability of proposed AI features. Collaborate closely with Principal Data Scientists and Principal Engineers to ensure that the MLOps framework supports the full scope of AI workflows and model interaction layers. What excites us? Weve moved past experimentation. We have live AI features and a strong pipeline of customers More ❯

experience 7 - 10 years+ experience of Consulting in Data Engineering, Data Platform and Analytics Deep experience with Apache Spark, PySpark CI/CD for Production deployments Working knowledge of MLOps Strong experience with Optmisations for performance and scalability These roles will be paid at circa £600 - £700 per day depending on skills and experence and can interview and start at More ❯

with people who are passionate about data and its ability to empower and improve lives. Work with cross-functional teams to deliver production level ML/AI solutions. Implement MLOps with a focus on versioning and data security. Champion Machine Learning across the business. Mentor junior members of the team. Profit share bonus. Skills and Experience Python. MLOps. Strong communication More ❯

plus Demonstrated experience building APIs to support analytics/research/actuarial functions in an insurance company setting Experience with cloud technologies like Snowflake and Databricks Familiarity with the MLOps framework Exceptional collaboration and relationship building skills More ❯

plus Demonstrated experience building APIs to support analytics/research/actuarial functions in an insurance company setting Experience with cloud technologies like Snowflake and Databricks Familiarity with the MLOps framework Exceptional collaboration and relationship building skills More ❯

prompt practices. Proven change leadership: you have driven ≥50% adoption with training and comms. Governance mindset: you have designed guardrails that pass risk and legal review. Working grasp of MLOps/GenAIOps (CI/CD, model registry, data lineage, evaluation). Clear communicator: you can brief boards and coach non-technical teams. Comfortable in a matrix: you align marketing, data More ❯

prompt practices. Proven change leadership: you have driven ≥50% adoption with training and comms. Governance mindset: you have designed guardrails that pass risk and legal review. Working grasp of MLOps/GenAIOps (CI/CD, model registry, data lineage, evaluation). Clear communicator: you can brief boards and coach non-technical teams. Comfortable in a matrix: you align marketing, data More ❯

Engineers (Junior to Mid-Level) who want to work with the latest in LLMs, co-pilots, agentic workflows, RAGs , and more, while also applying real data science, ML, and MLOps skills in live enterprise environments. What You'll Do: Work directly with enterprise clients to design and deploy custom AI agents Tackle complex business problems with intelligent, scalable solutions Blend More ❯

covers the full spectrum of Artificial Intelligence and Machine Learning expertise - from cutting-edge research to scalable infrastructure deployment. It includes academic-leaning AI researchers, production-grade ML engineers, MLOps experts who bridge the gap between models and systems, and cloud infrastructure professionals who build the backbone these technologies run on. We specialise in connecting innovative companies with the talent More ❯

the UK. Your role at Gemba: We are seeking software engineers eager to collaborate closely with technical users within a key Gemba customer. You will work with cutting-edge MLOps, AI, and cloud-native infrastructure to deliver mission-critical solutions. Your skills and experience: We welcome candidates from diverse technical backgrounds who are passionate about learning. Our team primarily works More ❯

to enhance service delivery and operational efficiency. Operational Excellence Ensure high availability, scalability, and resilience of all client-facing and internal systems. Implement best-in-class ITIL, DevOps, and MLOps practices. Manage vendor relationships and technology partnerships to maximize value and innovation. People & Culture Build and mentor a high-performing, diverse technology team. Foster cross-functional collaboration and alignment between More ❯

Learning (Azure ML), covering training, testing, and deployment to production. * Select appropriate ML techniques (e.g., classification, regression, ensemble methods, survival models) to improve predictions and automate core processes. 2. MLOps & Model Lifecycle Management * Own the end-to-end ML lifecycle using Azure ML pipelines, Data Factory (ADF), and ML tracking tools. * Implement CI/CD pipelines with Azure DevOps, ensuring … Training & Innovation * Clearly communicate ML model behaviour, performance, and limitations to both technical and non-technical stakeholders. * Support and upskill colleagues in the use of Azure ML tools and MLOps practices. * Explore and propose innovations based on emerging trends in Azure AI/ML and financial analytics. Essential Criteria * Demonstrated experience deploying and managing production machine learning models using Azure … Machine Learning. * Strong grasp of MLOps principles, including CI/CD, model monitoring, and retraining pipelines. * Experience with Python, SQL, and Azure data services such as Data Factory, Blob Storage, or Data Lake. * Proficiency with DevOps tools (e.g., Git, Azure DevOps) and version control workflows. * Familiarity with reporting and visualisation tools (e.g., Power BI). * Strong communication skills, with the More ❯

Learning (Azure ML), covering training, testing, and deployment to production.* Select appropriate ML techniques (e.g., classification, regression, ensemble methods, survival models) to improve predictions and automate core processes. 2. MLOps & Model Lifecycle Management * Own the end-to-end ML lifecycle using Azure ML pipelines, Data Factory (ADF), and ML tracking tools.* Implement CI/CD pipelines with Azure DevOps, ensuring … Training & Innovation * Clearly communicate ML model behaviour, performance, and limitations to both technical and non-technical stakeholders.* Support and upskill colleagues in the use of Azure ML tools and MLOps practices.* Explore and propose innovations based on emerging trends in Azure AI/ML and financial analytics. Essential Criteria * Demonstrated experience deploying and managing production machine learning models using Azure … Machine Learning.* Strong grasp of MLOps principles, including CI/CD, model monitoring, and retraining pipelines.* Experience with Python, SQL, and Azure data services such as Data Factory, Blob Storage, or Data Lake.* Proficiency with DevOps tools (e.g., Git, Azure DevOps) and version control workflows.* Familiarity with reporting and visualisation tools (e.g., Power BI).* Strong communication skills, with the More ❯

data science can deliver measurable value. Create powerful visualisations and present findings to technical and non-technical stakeholders. Build, deploy, and maintain robust data pipelines and production-grade models (MLOps). Champion ethical data practices, governance, and high-quality documentation. Stay up to date with AI and data science innovations, bringing cutting-edge thinking into the business. As a Data … data science can deliver measurable value. Create powerful visualisations and present findings to technical and non-technical stakeholders. Build, deploy, and maintain robust data pipelines and production-grade models (MLOps). Champion ethical data practices, governance, and high-quality documentation. Stay up to date with AI and data science innovations, bringing cutting-edge thinking into the business. Experience required A … e.g., TensorFlow, PyTorch, Scikit-learn). Strong skills in data storytelling, problem-solving, and working with both structured and unstructured data. Experience with APIs, cloud platforms (especially AWS), and MLOps practices is a strong advantage. A collaborative, curious mindset with excellent communication skills and a passion for innovation. Benefits include: Salary £55,000 dependant on experience Hybrid working, two days More ❯

solutions. Assist in fine-tuning large language models (LLMs) and retrieval-augmented generation (RAG) systems. Optimize model performance and work on deployment strategies using cloud-based AI solutions. Support MLOps best practices to streamline AI development workflows. Stay updated with the latest advancements in machine learning by regularly reviewing academic literature and research papers. Capable of translating theoretical insights into … personalization engines. Familiarity with production-scale vector databases (e.g., QDrant, Pinecone, Weaviate). Understanding of AI model interpretability and ethical AI considerations. Exposure to real-time AI applications and MLOps workflows. Why Join Us? Work alongside industry experts on cutting-edge AI projects. Opportunity to grow and advance in a fast-paced, innovative environment. Competitive compensation, benefits, and professional development More ❯

large (AI) transformational journeys BCG does for its clients. Often involves the following engineering disciplines : Cloud Engineering Data Engineering (not building pipelines but designing and building the framework) DevOps MLOps/LLMOps Often work with the following technologies : Azure, AWS, GCP Airflow, dbt, Databricks, Snowflake, etc. GitHub, Azure DevOps and related developer tooling and CI/CD platforms, Terraform or … other Infra-as-Code MLflow, AzureML or similar for MLOps; LangSmith, Langfuse and similar for LLMOps The difference to our "AI Engineer" role is: Do you "use/consume" these technologies, or are you the one that "provides" them to the rest of the organization. What You'll Bring TECHNOLOGIES: Programming Languages: Python Experience with additional programming languages is a More ❯

models using machine learning and statistical methods Guide the team in delivering production-grade solutions in partnership with data engineering Champion best practices in model development, evaluation and deployment (MLOps) Communicate results and recommendations clearly to senior stakeholders across the business Mentor junior members of the team and contribute to team development and technical standards Help shape the roadmap for … Python skills and familiarity with libraries such as scikit-learn, pandas, NumPy, PyTorch or TensorFlow Advanced SQL skills and experience working with complex, high-volume datasets Practical experience with MLOps tools and practices for deploying and maintaining models Experience leading projects and collaborating across cross-functional teams Ability to influence stakeholders and communicate complex ideas clearly to non-technical audiences More ❯