176 to 200 of 7,394 Machine Learning Jobs in the UK

applying AI technologies to real-world business challenges. Requirements Key Responsibilities Design, develop, and maintain high-performance Java-based applications Integrate AI/ML models into production-grade backend systems Build RESTful APIs and microservices using modern Java frameworks Collaborate with data scientists to deploy and scale machine learning … deploy AI-powered services Contribute to architecture decisions and technical roadmaps Design, develop, and maintain high-performance Java-based applications Integrate AI/ML models into production-grade backend systems Build RESTful APIs and microservices using modern Java frameworks Collaborate with data scientists to deploy and scale machine learning ...

financial services (banking, capital markets, insurance, or fintech) to design, build, and scale GenAI-powered data solutions. The role combines modern data engineering, ML/LLM integration, and domain-specific financial knowledge to deliver enterprise-grade AI platforms. You will lead the development of data pipelines, vector databases, retrieval systems … your home; noting that you will be unable to work at home 100% of the time.**Key Responsibilities**:* Design, develop, and deploy AI/ML and Generative AI solutions using Python.* Build, train, evaluate, and fine-tune machine learning and large language models (LLMs) using frameworks such ...

design of scalable, robust agentic and data architectures that align with business objectives. Establish technical standards, next practice, and development methodologies for AI/ML projects. AI/ML Development & Innovation: Lead the development of advanced AI/ML models and systems, from research through to production deployment. Drive innovation … reliability, scalability, security, and compliance. Lead technical reviews and provide guidance on complex AI engineering challenges. Technical Expertise: Deep expertise in AI/ML frameworks—TensorFlow, PyTorch, Scikit‐Learn, Transformers—cloud platforms (AWS, Azure, GCP), and modern software engineering practices. Strong background in deep learning, natural language processing, computer ...

practices (SLOs, error budgets, runbooks) and leads production incident response and post-incident review Builds and operates observability across the team's AI/ML services (metrics, logging, tracing, alerting) Automates infrastructure provisioning and configuration through infrastructure-as-code Implements operational security, secrets management, and access controls to firm-wide … environment Master's degree in Computer Science, Engineering, or a related technical field (or equivalent applied experience) Preferred qualifications, capabilities, and skills Experience operating ML/LLM workloads in production (MLOps, inference reliability, cost/performance management) Experience within financial services technology Familiarity with JPM-internal platform, cloud, and observability ...

Design and build statistical, machine learning, and AI tools, alongside investigative data pipelines, for integration into existing or new reliability infrastructure.Apply data science to improve Google Cloud Platform reliability in partnership with the Platform Reliability Engineering (PRE) organization, focusing on change supervision and rollouts.Collaborate closely with engineering … broad spectrum of reliability problems where data-driven approaches can be applied, combining subject matter expertise with statistical, predictive, and generative AI/ML methods to improve reliability in ways that truly matter to Google's users and customers.In this role, you will design statistical and machine learning ...

connected devices, cloud services, data platforms, and engineering teams. As the capability matures, the role will provide a clear development path into MLOps and ML platform engineering, supporting model deployment, model lifecycle controls, ML pipeline automation, and governed promotion of machine learning solutions from development into production. … GitHub, Azure DevOps and related platform tooling. Help maintain Azure-based development, test, QA and production environments used by digital, IoT, data and ML platforms. Contribute to secure platform configuration, access control, secrets management, monitoring and operational governance. Monitor platform health, investigate operational issues and contribute to improving reliability, supportability ...

deployment pipelines, model serving, containerisation, orchestration, and environment managementSets the standard for reliability, observability, and operational excellence across the team's production AI/ML servicesBuilds the tooling and paved paths that let AI engineers ship agentic AI and ML products safely and quicklyImplements platform-level security, secrets management … containerisation, and cloud-native deployment patternsStrong CI/CD experience and a track record of building deployment and release automationExperience serving, scaling, and monitoring ML models or data-intensive services in production (MLOps)Practical experience with observability tooling (metrics, logging, tracing) and production incident responseExperience operating ML/LLM workloads ...

pipelines, model serving, containerisation, orchestration, and environment management Sets the standard for reliability, observability, and operational excellence across the team's production AI/ML services Builds the tooling and paved paths that let AI engineers ship agentic AI and ML products safely and quickly Implements platform-level security, secrets … cloud-native deployment patterns Strong CI/CD experience and a track record of building deployment and release automation Experience serving, scaling, and monitoring ML models or data-intensive services in production (MLOps) Practical experience with observability tooling (metrics, logging, tracing) and production incident response Experience operating ML/ ...

clients, understanding their needs, and translating them into effective outcomes. Key Responsibilities Design, develop, and maintain high-performance Java-based applications Integrate AI/ML models into production-grade backend systems Build RESTful APIs and microservices using modern Java frameworks Collaborate with data scientists to deploy and scale machine … deploy AI-powered services Contribute to architecture decisions and technical roadmaps Design, develop, and maintain high-performance Java-based applications Integrate AI/ML models into production-grade backend systems Build RESTful APIs and microservices using modern Java frameworks Collaborate with data scientists to deploy and scale machine learning ...

business teams, and product partners (e.g. across Investment, Client experience, and surfacing IPB-first use cases) to discover and frame high-value AI/ML opportunities Rapidly prototypes and builds agentic AI and LLM-powered solutions full-stack and end-to-end (backend, data, and lightweight interfaces as needed … delivery Champions the firm's culture of diversity, Opportunity, inclusion, and respect Required qualifications, capabilities, and skills Formal training or certification on AI/ML engineering concepts and applied experience Advanced Python and full-stack/generalist engineering ability - backend, data, and lightweight front-end - to ship a working ...

Vacancy detailSenior AI/ML Engineer90,000 - 110,000 GBPNo Hybrid WORKINGLocation: UK wide , Central London, Greater London – United Kingdom Type: PermanentSenior AI Engineer – London (Remote)We're looking for an experienced AI Engineers to lead the next generation of AI-powered learning experiences. You'll play a pivotal … defining our AI strategy, architecting scalable solutions, and bringing innovative products to millions of learners.This is an opportunity to join a fast-growing online learning platform where your work will directly influence how people develop new skills, advance their careers, and achieve their goals.The RoleAs an AI Engineer ...

lessons learned fast. Be the kind of leader who creates that energy and channels it into things that last. Push the frontier of AI, ML, and intelligent systems Go beyond adopting AI tools — define how intelligent systems become structural, load-bearing parts of the platforms your teams own. Whether that … powered workflows, ML models embedded in analytical processes, agentic automation, or AI-augmented developer tooling, bring the depth to evaluate, architect, and champion these capabilities with real technical conviction. If you've built ML systems from the ground up, that experience will resonate deeply here — and you'll have ...

system architecture.Experience pair programming collaboratively with other developersExposure to or interest in working with modern AI systems (LLMs, agent frameworks, cloud deployment). ML experience is a plus but not required.Nice to have: Experience with cloud deployment (Azure/AWS/GCP), containerization (Docker), frontend frameworks (React), CI/… large-scale, complex projects—including open-sourcing some of these frameworks, such as Kedro and ARK—and developing innovative, industry-specific AI/ML solutions that embed our distinctive technical approach and business knowledge for enterprise clients.As a Software Engineer II, you will collaborate with interdisciplinary teams to design ...

will own the data and model layers of our client projects: designing and building the pipelines that feed AI systems, developing and deploying ML and LLM‐powered solutions, and standing up the RAG pipelines and agentic AI systems. Your role will include planning, tool use, and human‐in‐the‐loop … proof‐of‐concept builds for new client engagements, including AI readiness and data assessments Experience 3‐5 years of professional experience across AI/ML engineering and/or data engineering in a commercial or consultancy setting Strong, demonstrable proficiency in Python for data and ML work Advanced ...

best practices to enhance team efficiency and delivery excellence.Connect to your skills and professional experienceTechnical Proficiency:Proven experience with Python and popular AI/ML libraries such as TensorFlow, PyTorch, scikit-learn.Strong understanding of machine learning algorithms, deep learning architectures, and statistical modelling techniques.Experience with cloud computing … platforms (e.g., AWS, Azure, GCP or equivalent) and their AI/ML services.Proficiency in data manipulation and analysis using SQL and big data technologies (e.g., Spark, Hadoop) is a plus.Experience with MLOps tools and best practices for model deployment, monitoring, and governance.Education and Experience:Bachelor's or Master ...

reliability practices (SLOs, error budgets, runbooks) and leads production incident response and post-incident reviewBuilds and operates observability across the team's AI/ML services (metrics, logging, tracing, alerting)Automates infrastructure provisioning and configuration through infrastructure-as-codeImplements operational security, secrets management, and access controls to firm-wide standardsPartners … high-availability environmentMaster's degree in Computer Science, Engineering, or a related technical field (or equivalent applied experience)Preferred qualifications, capabilities, and skillsExperience operating ML/LLM workloads in production (MLOps, inference reliability, cost/performance management)Experience within financial services technologyFamiliarity with JPM-internal platform, cloud, and observability tooling ...

organization. We partner with teams across the firm to solve some of the most complex and consequential challenges in financial services, applying generative AI, machine learning, optimization, and statistics to deliver measurable business impact. Our mission is simple but ambitious: combine human and machine intelligence to revolutionize … including supporting their career growth and development. Strong familiarity with Python programming, and hands-on experience guiding solutions architecture for production-grade AI/ML systems. Strong theoretical background in and practical experience using AI, machine learning, optimization, or statistical techniques. Experience assessing performance of machine learning ...

others to deliver advanced AI and data solutions at citizen scale. Our 150-strong AI and Data Practice brings together deep expertise in machine learning, generative AI, agentic AI and data. We are pioneers in responsible AI, having authored the UK government’s AI Cyber Security Code … 2.1 degree in Computer Science, AI, Data Science, Statistics or in a similar quantitative field. Demonstrable experience of deploying modern AI/ML solutions into production, including prompt engineering, retrieval-augmented generation (RAG), model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG and drift detection). Strong Python skills ...

DescriptionCertificationsNVIDIA certifications (NCP-AI Infrastructure, or NVIDIA Deep Learning Institute credentials) — strongly preferred.Cloud AI/ML certification: AWS Certified Machine Learning – Specialty, Microsoft Certified: Azure AI Engineer Associate, or Google Professional Machine Learning Engineer — at least one preferred.Kubernetes: CKA or CKAD — preferred.TOGAF 9/… ExperienceBachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent demonstrable experience.Advanced degree (MS in CS/AI/ML or related) beneficial but not required given sufficient hands-on depth.Continuous, demonstrable learning in AI — publications, open-source contributions, conference speaking, or lab-based ...

others to deliver advanced AI and data solutions at citizen scale. Our 150-strong AI and Data Practice brings together deep expertise in machine learning, generative AI, agentic AI and data. We are pioneers in responsible AI, having authored the UK government’s AI Cyber Security Code … 2.1 degree in Computer Science, AI, Data Science, Statistics or in a similar quantitative field. Demonstrable experience of deploying modern AI/ML solutions into production, including prompt engineering, retrieval-augmented generation (RAG), model evaluation, and monitoring using metrics (e.g. precision, recall, NDCG and drift detection). Strong Python skills ...

others to deliver advanced AI and data solutions at citizen scale. Our 150-strong AI and Data Practice brings together deep expertise in machine learning, generative AI, agentic AI and data. We are pioneers in responsible AI, having authored the UK government’s AI Cyber Security Code … Computer Science, AI, Data Science, Statistics or in a similar quantitative field. Proven experience of leading multi-disciplinary teams to deliver AI/ML solutions to production. Demonstrable experience of technical leadership for AI delivery including architecture, product design principles and engineering excellence. Experience with the latest AI engineering approaches ...

production standard, with object oriented design, testing and code review as standard practice Models you've personally taken into production and supported in life Machine learning across the usual toolkit: scikit … learn, pandas, NumPy, statsmodels or similar MLOps and CI/CD in practice. Pipeline orchestration, model versioning, automated deployment, monitoring and retraining Cloud based ML delivery, ideally Azure (Azure ML, Azure DevOps). Strong AWS or GCP considered SQL and relational data modelling against large datasets Version control, branching strategy ...

broad spectrum of reliability problems where data-driven approaches can be applied, combining subject matter expertise with statistical, predictive, and generative AI/ML methods to improve reliability in ways that truly matter to Google's users and customers. In this role, you will design statistical and machine learning … rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible. Responsibilities Design and build statistical, machine learning, and AI tools, alongside investigative data pipelines, for integration into existing or new reliability infrastructure. Apply data science to improve Google Cloud ...

capabilities or “engines” that integrate directly into Anaplan Applications to solve complex enterprise business problems. In this role, you will develop best-in-class ML models and pipelines to drive automated, actionable insights at scale. Beyond core development, you will act as a key technical partner for our customers. … will play a highly visible role in driving customer onboardings, getting hands-on with client data to customize and fine-tune our ML engines so they seamlessly adapt to complex business realities and specific customer needs. Your Impact Customer Onboarding & Engine Customization: Lead the technical deployment of our engines ...

clients and with the communities in which we work and live. It is personal to all of us.” – Julie Sweet, Accenture CEOKey responsibilities · Deploy machine learning models to production and implement measures to monitor their performance · Implement ETL pipelines and orchestrate data flows using batch and streaming technologies … Infrastructure as Code tools (e.g. Terraform or CloudFormation) · Strong understanding of data modelling and system architecture · Demonstrable experience on at least one AI/ML project · Knowledge of common machine learning frameworks and models · A good understanding of approaches to monitoring ML models in production · Experience leading ...