851 to 875 of 6,448 Machine Learning Jobs in the UK

data platform that enables both the decisions we make internally and the data-driven features our customers rely on. Working with the Data, ML, Platform, and Product Engineering teams, you will keep ingestion, modelling, and orchestration dependable, and define the modelling, testing, and documentation standards the data function depends on. … data with confidence. What You’ll Do Set the architectural direction of our data platform so it serves analytics, product engineering, and AI/ML from one coherent design, collaborating with Platform and ML engineering on shared infrastructure. Design the dbt models, semantic layer, and quality standards the data function ...

across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. AIS spans multiple workstreams — agent evaluation and oversight, orchestration and tool … structured reasoning over enterprise data. Design and implement RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking. Build and maintain integrations between retrieval and ML components and diverse enterprise data sources, vector databases, APIs, and services. Develop context retrieval systems that balance recall, precision, latency, and cost. Build evaluation frameworks ...

team. You'll report to a Leader of Engine Development and collaborate with both game and central technology engineers and researchers to bring ML models into the hands of our players by deploying them directly into EA's games. Responsibilities: Design, build, and maintain robust end-to-end solutions … running machine learning models efficiently on a variety of devices. Partner with ML experts across EA to help adopt and scale new models and architectures optimized for on-device performance. Integrate ML solutions into our proprietary tools and game runtime environments on consoles, PCs, and mobile devices. Write ...

team, you will leverage deep operational experience to deliver complex change across report automation, platform enhancements and migrations, and the introduction of AI/ML-enabled capabilities. You will work closely with Operations, Technology, and Compliance to modernize processes end-to-end, strengthen controls and data quality, and ensure change … data flows. Lead delivery of transformation workstreams: report automation, platform rollouts, migrations, and re-engineering. Drive adoption of intelligent automation (including AI/ML) in partnership with Technology and Data Science. Own the delivery lifecycle: requirements, impact assessment, UAT coordination, cutover planning, and stabilization. Deliver change with minimal BAU disruption ...

Production Solution Delivery: Lead impactful customer technical projects by delivering production-grade systems, designing and building reference architectures, custom applications and data ingestion and ML/AI model integrationTransformational Impact: Guide strategic customers as they implement transformational big data projects including end-to-end design, build and deployment of industry … oneDeep experience with distributed computing with Apache Spark and knowledge of Spark runtime internalsFamiliarity with CI/CD for production deploymentsWorking knowledge of MLOps, ML/AI models and AI APIsDesign and deployment of performant production end-to-end data architectures and applications that combine data pipelines, ML/ ...

meaningful, actionable insights. You will be accountable for the team's people, execution, engineering quality, and delivery while partnering closely with Product, AI/ML, Data, DevOps, and other engineering teams. You will help translate our strategy into an executable engineering roadmap, create an environment where engineers can do their … execution across the software development lifecycle, from requirements and technical design through development, testing, deployment, and ongoing production support. Partner with Product, AI/ML, Data, and business stakeholders to translate product strategy and customer needs into clear engineering priorities and deliverables. Work with technical leaders and engineers to establish ...

Location: London, United Kingdom Industry: Insurance Overview We are seeking an AI Platform Engineer to design, build and operate a secure, scalable AI/ML platform supporting insurance use cases including pricing, claims automation, fraud detection, customer analytics and document intelligence. You will partner with engineering, data, security and risk … maintain platform capabilities for model training, feature engineering, deployment and observability across cloud and on-prem environments. Build CI/CD pipelines for ML (MLOps) including automated testing, packaging, model registry workflows and promotion across environments. Implement containerised deployments (Docker/Kubernetes) and orchestration for batch and real-time inference ...

technical debates that shape the future of Nevis' technical direction. Who You Are 4+ years of experience in Python engineering, ideally building AI platforms, ML infrastructure, or applied AI products. Deep expertise in Python, asynchronous programming, system design, and backend engineering fundamentals. Hands-on experience building production systems involving … ownership mindset. Excellent communication and collaboration skills. Comfortable operating in a fast-paced, high-growth environment. Nice to Have Experience shipping applied AI or ML-powered products in production. Knowledge of vector databases, feature stores, or streaming systems. Background in fintech, financial services, wealth management, or startups. Why Join Nevis ...

feasibility, vendor capability, total cost of ownership, and time‐to‐value, then manage the chosen delivery path to completion. Work with data science and ML engineers, managing them to defined milestones and value targets. Monitor deployed solutions and manage model performance, iteration, and stakeholder reporting. Change Strategy, Narrative & Leadership Enablement … 8+ years in product/project management, AI delivery, or data science role, with hands‐on experience building and deploying AI or ML solutions in a business context. Proven track record of owning AI or data science use cases from identification through to live production - not advisory or strategy only. ...

Strong commercial experience with Python and SQL. Solid experience working on data science or AI projects. Experience with AWS, Azure or GCP Experience with ML and modern LLM/RAG systems. Strong communication skills and ability to collaborate with international teams Experience delivering data-driven solutions in a production environment ...

Databricks Platform (or demonstrated ability to achieve proficiency rapidly) with a developing technical specialisation in one area (e.g. real-time/streaming, ML/AI, data governance, migrations)Ability to contribute to architecture discussions with senior technical stakeholders: whiteboarding, design reviews, and trade-off analysisTrack record of driving platform adoption … Computer Science, Engineering, or a quantitative discipline (or equivalent experience)Willingness to travel up to 30% as neededNice to HaveDatabricks certifications (Data Engineer, ML, Platform)Cloud provider certifications at professional level (e.g. AWS Certified Solutions Architect – Professional or AWS Certified Data Engineer; Azure Solutions Architect Expert; Google Professional Cloud Architect ...

aspects of the Software Development Life Cycle, including design, development, testing, and operational stability in production environments Practical understanding of AI/ML concepts and experience integrating or embedding AI/ML capabilities into business applications or data workflows, with the ability to identify opportunities where intelligent automation or predictive ...

Databricks Platform (or demonstrated ability to achieve proficiency rapidly) with a developing technical specialisation in one area (e.g. real-time/streaming, ML/AI, data governance, migrations) Ability to contribute to architecture discussions with senior technical stakeholders: whiteboarding, design reviews, and trade-off analysis Track record of driving platform … Science, Engineering, or a quantitative discipline (or equivalent experience) Willingness to travel up to 30% as needed Nice to Have Databricks certifications (Data Engineer, ML, Platform) Cloud provider certifications at professional level (e.g. AWS Certified Solutions Architect – Professional or AWS Certified Data Engineer; Azure Solutions Architect Expert; Google Professional Cloud ...

practicesProficient in all aspects of the Software Development Life Cycle, including design, development, testing, and operational stability in production environmentsPractical understanding of AI/ML concepts and experience integrating or embedding AI/ML capabilities into business applications or data workflows, with the ability to identify opportunities where intelligent automation ...

retrieval‐augmented generation (RAG). Strong LLM application skills — prompt engineering, evaluation, and guardrails for reliable, safe outputs. Strong Python for AI/ML, plus solid backend engineering (Go and/or Kotlin) to productionize systems at scale. Model serving and LLM inference, with the cloud infrastructure … management at scale. Data pipelines and orchestration (Airflow, Prefect) and warehouses like Databricks, Snowflake, or BigQuery. Spanish proficiency. Tech Stack Languages — Python (AI/ML), Go and Kotlin (platform services). AI & LLMs — LLM APIs and open models, agent frameworks, MCP, RAG. AI evaluation & observability — LangFuse, LangSmith, Braintrust, MLflow. Data ...

practical experience delivering system design, application development, testing, and operational stability at enterprise scale Hands-on experience designing and deploying production AI/ML systems, including LLM-based applications and agentic architectures with tool use, memory, and multi-step reasoning in regulated environments Expert in one or more programming languages …/or Java Advanced knowledge of software application development and technical processes, with considerable depth in one or more disciplines (e.g., cloud, AI/ML, data engineering) Experience in large-scale data processing, microservices, API design, Kafka, Redis, MemCached, observability tools (Dynatrace, Splunk, Grafana), and orchestration frameworks (Airflow, Temporal) Advanced ...

architecture must evolve with it. A self‐led learner who stays current with cloud‐native patterns, integration architectures, event‐driven systems, and AI/ML platform capabilities Essential 5+ years of experience as a Solutions Architect or in a senior technical design role, with direct accountability for architecture decisions … Familiarity with large‐scale modernisation programmes, particularly where modern platforms must integrate with or incrementally replace established systems. Hands‐on experience with AI/ML platform integration, particularly document processing, intelligent automation, or rules‐based adjudication engines. Experience working with US‐headquartered companies, building relationships with teams in other locations ...

Technical Oversight End-to-End Data Programme Delivery: Drive the full lifecycle of complex data initiatives (Data Lakehouses, Data Mesh, Real-time Streaming, and ML/GenAI pipelines) from inception to production, ensuring alignment with GlobalLogic’s Delivery Framework and client governance. Architecture & Technical Alignment: Bridge the gap between … Data & AI Architecture Fluency: Deep understanding of modern data stacks (Data Lakehouses, Data Mesh, Real-time Streaming, ETL/ELT pipelines) and AI/ML lifecycle management (MLOps, GenAI integration, Model Governance). Ability to challenge architectural patterns and supervise technical teams without acting as the hands-on architect. Cloud ...

analytics technology landscapes and big data solutions. A bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related field. AI/ML Familiarity: Understanding of how to integrate AI/ML models into data architectures. Platform‐specific data certifications (e.g., GCP, Snowflake). Our Commitment to Inclusion ...

Proficiency in Shell or PowerShell scripting and Python Experience with Agile/Scrum practices Knowledge of Docker, Kubernetes, and AWS Exposure to AI/ML-powered tooling in software engineering or DevOps workflows Strong communication and stakeholder management skills Willingness to work in a hybrid model in London, UK Nice ...

passionate about designing and deploying intelligent machine learning solutions that drive business impact? Do you enjoy leading teams, building scalable ML systems, and turning complex data into innovative products and services? About the team: We are a software engineering team responsible for developing and supporting business-critical platforms … wider organisation. About the role: This position serves as a subject matter expert for Machine Learning Engineering, supporting production AI/ML, LLM/RAG, and agentic workflow capabilities for legal content products. In addition to writing code on complex systems, this position provides technical direction on architecture ...

degree in Computer Science, Computer Engineering, Electrical Engineering, Data Science, or a related technical field. 2+ years of experience in software engineering, AI/ML engineering, backend development, or adjacent domains, with strong software engineering fundamentals and the ability to build production-grade systems. Strong proficiency in Python, plus experience … LangChain, AutoGen, or Semantic Kernel. Experience with video, audio, TV, or edge-device environments, especially where latency, cost, and hardware constraints matter. Familiarity with ML/DL frameworks such as PyTorch or TensorFlow. Research experience, paper implementation experience, or a habit of applying emerging GenAI techniques pragmatically to real problems. ...

human-in-the-loop controls. Evaluation: Using regression sweeps, canary testing and LLM-as-judge scoring to determine what is ready to ship. ML infrastructure: Building versioned training pipelines, model registries, scheduled jobs and deployments using Azure ML. What you’ll be responsible for Owning the development and continuous improvement … state management. Experience evaluating LLM applications using approaches such as gold datasets, regression testing, LLM-as-judge scoring and canary testing. Experience with cloud ML platforms - ideally Azure ML, Functions and Blob Storage - or the willingness to transfer your knowledge to Azure. Experience with Git-based workflows, CI/ ...

projects while ensuring consistency with overall strategy, commitments, and goals. You will gain fantastic experience working in the cloud with data analytics, AI and Machine Learning. The successful candidate will have 10 plus years of working with data projects, including leading teams and presenting business outcomes to key stakeholders. ...

work closely with the Head of Hardware/Devices’ engineering team, whose devices you bring to life in software, and with the Lead of ML & AI on integrating models at the edge. You’ll be involved across the full lifecycle from requirements through implementation, testing and field deployment working directly ...