251 to 275 of 414 MLOps Jobs in the UK

Science Engineer to design, develop, and deliver AI and analytics solutions aligned with the organisations Data & AI strategy. Key Responsibilities * End-to-end development of AI/ML solutions. * MLOps practices: CI/CD, model monitoring, retraining. * Use of open-source and enterprise tools (LangChain, Azure OpenAI, Databricks). * Generative AI features: embeddings, RAG, AI agents. * Clean, testable code with … years in production-level AI/ML delivery. * Legal/professional services experience is a plus. * AI/ML frameworks: PyTorch, TensorFlow, LangChain. * Cloud: Azure (preferred), AWS, GCP. * MLOps: CI/CD, model lifecycle, monitoring. * Generative AI: LLMs, RAG, chat agents. * Data engineering alignment: ETL, governance. * Strong coding, communication, and collaboration skills. * Strategic thinking, problem-solving, and stakeholder engagement. In More ❯

warehouse architecture and development, infrastructure as code (IaC) using Terraform , and data extraction from both structured and unstructured data sources (e.g. websites). Knowledge using the Microsoft Azure ecosystem, MLOps, Kubernetes , and other modern data engineering practices. Core Responsibilities Data Architecture & Development: Leverage best practices in schema design, partitioning, and optimisation for efficient storage and retrieval. Build and maintain data … data solutions. Infrastructure as Code (IaC): Use Terraform to automate the provisioning and management of cloud infrastructure. Define reusable and modular Terraform configurations to support scalable deployment of resources. MLOps: Collaborate with data scientists and machine learning engineers to operationalise machine learning models. Implement CI/CD pipelines for machine learning workflows, ensuring efficient model deployment and monitoring. Containerisation and … track record of designing and implementing data lakes and warehouses (experience with Azure is a plus). Demonstrated experience with Terraform for infrastructure provisioning and management. Solid understanding of MLOps practices, including model training, deployment, and monitoring. Hands-on experience with Kubernetes and containerised environments. Technical Skills: Proficiency in programming languages such as Python & SQL. Experience with distributed computing frameworks More ❯

prototype, and productionize complex, stateful applications and workflows powered by LLMs. Model Integration & Deployment: Fine-tune, evaluate, and deploy LLMs and other machine learning models into production environments using MLOps best practices. What did we order? Experience with cloud platforms (AWS, GCP, or Azure). Bachelor's or Master's degree in Computer Science, AI, Engineering, or a related field. … Experience with fine-tuning open-source LLMs (e.g., Llama, Mistral, Falcon). Familiarity with MLOps tools and principles for deploying and monitoring models in production. Proven professional experience as a Machine Learning Engineer, with a strong portfolio of projects. Hands-on experience implementing RAG pipelines and a deep understanding of the underlying architecture. Demonstrable expertise in building applications with LangChain More ❯

and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). … loading (ETL). Experience with SQL databases (SQL Server), data validation, cleansing workflows, scheduling tools (Azure Data Factory), and ensuring data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/ More ❯

and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). … loading (ETL). Experience with SQL databases (SQL Server), data validation, cleansing workflows, scheduling tools (Azure Data Factory), and ensuring data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/ More ❯

and deploy production-grade AI systems tailored to audit applications. Contribute to architectural decisions and solution governance. Write clean, efficient, and scalable code that adheres to software engineering principles, MLOps practices, and cloud-native development. AI Solution Delivery: Own the implementation of ML pipelines, APIs, and data integration workflows. Operational Excellence: Contribute to defining reusable development patterns, enforcing coding standards … and implementing MLOps best practices that support version control, performance optimisation, and maintainability. Cross-Disciplinary Collaboration: Work side-by-side with data scientists, product managers, platform engineers, and QA teams to align on technical requirements, delivery timelines, and integration plans. Provide hands-on contributions to ensure AI capabilities are smoothly embedded within core audit platforms and services. Capability Building & Knowledge More ❯

and deploy production-grade AI systems tailored to audit applications. Contribute to architectural decisions and solution governance. Write clean, efficient, and scalable code that adheres to software engineering principles, MLOps practices, and cloud-native development. AI Solution Delivery: Own the implementation of ML pipelines, APIs, and data integration workflows. Operational Excellence: Contribute to defining reusable development patterns, enforcing coding standards … and implementing MLOps best practices that support version control, performance optimisation, and maintainability. Cross-Disciplinary Collaboration: Work side-by-side with data scientists, product managers, platform engineers, and QA teams to align on technical requirements, delivery timelines, and integration plans. Provide hands-on contributions to ensure AI capabilities are smoothly embedded within core audit platforms and services. Capability Building & Knowledge More ❯

ALTEN , an engineering and technology consultancy, We are a leading Engineering and IT consultancy operating across 30 countries, making waves in all sectors: Aeronautics, Space, Defence, Security and Naval, Automotive, Rail and mobility, Energy and environment, Life Sciences and health More ❯

ALTEN , an engineering and technology consultancy, We are a leading Engineering and IT consultancy operating across 30 countries, making waves in all sectors: Aeronautics, Space, Defence, Security and Naval, Automotive, Rail and mobility, Energy and environment, Life Sciences and health More ❯

You will ideally have a strong academic foundation which has progressed into applied AI where you will be working with a plethora of technologies/projects including; RAG, LLM, MLOps MLFlow, Vertex AI and HuggingFace NLP with LangChain Python Agentic AI This is a client facing role where you will act as a consultant on AI transformation for the end More ❯

twice a week on-site 6 month initial contract Working with a financial services client who are looking to build a new AI/ML function. Looking for a MLOps Engineer to design, develop and implement AI and ML applications. Will require working cross-functionally to conceptualise, design, test and deploy AI projects. Azure AI/ML Engineer, key responsibilities More ❯

BERT, T5). Solid background in 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 instance and you will More ❯

BERT, T5). Solid background in 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 instance and you will More ❯

and scales AI, including generative and agentic technologies, end-to-end. Skills and Experience Proven leadership experience in Gen AI delivery Hands-on technical understanding of modern AI tooling, MLOps, and model deployment and generative/agentic AI frameworks Commercial mindset, experience bringing AI, including generative, use cases into production Strong communication skills and the ability to build and scale More ❯

ML models Able to be SC cleared - Must hold British passport (we have MoD projects) There are also some things that would be helpful to have like experience with MLOps and having worked in a startup. What do we offer? Upto £140k base salary Generous share options Flexible and hybrid working – 2 days in office (40 mins from Paddington) Interested More ❯

ML models Able to be SC cleared - Must hold British passport (we have MoD projects) There are also some things that would be helpful to have like experience with MLOps and having worked in a startup. What do we offer? Upto £140k base salary Generous share options Flexible and hybrid working – 2 days in office (40 mins from Paddington) Interested More ❯

but Valuable) Worked at a Pre-Seed or Seed-stage startup through to Series A Built or deployed software used by enterprise customers Experience with applied machine learning or MLOps (not just academic or notebook-based work) Exposure to event-driven architectures, streaming systems (Kafka, etc.), or real-time data pipelines Frontend experience in React, TypeScript, or similar modern frameworks More ❯

including conferences, publications, and industry forums About You • Proven record in AI and machine learning strategy and delivery, ideally within the financial or professional services sector • Strong experience with MLOps and scaling AI applications into production environments • Deep understanding of systems architecture and engineering principles, with experience leading full stack technology teams • Commercial awareness of how AI can drive growth More ❯

including conferences, publications, and industry forums About You • Proven record in AI and machine learning strategy and delivery, ideally within the financial or professional services sector • Strong experience with MLOps and scaling AI applications into production environments • Deep understanding of systems architecture and engineering principles, with experience leading full stack technology teams • Commercial awareness of how AI can drive growth More ❯

Senior Machine Learning/AI Engineer Position Overview: We are seeking a Senior Machine Learning/AI Engineer with expertise in Databricks, MLOps/LLMOps, and cloud-native architecture . The candidate must have recent experience implementing data science solutions in Databricks and be comfortable deploying web applications via containerized workflows (Docker, Kubernetes). This role involves building scalable AI … in production. Key Responsibilities: Design, develop, and deploy ML, Deep Learning, and LLM solutions. Implement scalable ML and data pipelines in Databricks (PySpark, Delta Lake, MLflow). Build automated MLOps pipelines with model tracking, CI/CD, and registry. Deploy and operationalize LLMs , including fine-tuning, prompt optimization, and monitoring. Architect secure ML/AI systems on Azure, AWS, or … 5+ years in ML/AI solution development. Recent hands-on experience with Databricks, PySpark, Delta Lake, MLflow . Experience with LLMs (Hugging Face, LangChain, Azure OpenAI) . Strong MLOps, CI/CD, and model monitoring experience. Proficiency in Python, PyTorch/TensorFlow, FastAPI/Flask . Cloud architecture experience: Azure preferred, AWS/GCP acceptable . Skilled in Docker More ❯

Machine Learning Engineer page is loaded Machine Learning Engineerlocations: UK, Yorktime type: Full timeposted on: Posted Yesterdayjob requisition id: R Job Type: Permanent Build a brilliant future with Hiscox Position: Machine Learning Engineer Reporting to: Lead Data Scientist Location: York More ❯

twice a week on-site 6 month initial contract Working with a financial services client who are looking to build a new AI/ML function. Looking for a MLOps Engineer to design, develop and implement AI and ML applications click apply for full job details 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 ❯

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 ❯

and vector search architectures Ability to build autonomous agents that interact with APIs, fetch public data and trigger external actions Experience deploying AI solutions on Azure Strong understanding of MLOps, data engineering and model lifecycle management Experience embedding AI features into a SaaS or operational technology platform Experience working in the manufacturing industry or with manufacturing, supply chain or inventory More ❯