51 to 75 of 229 Machine Learning Jobs in the City of London

notebooks, clusters); Azure Machine Learning models and endpoints; Power BI dashboards and dataset refreshes. Monitor and troubleshoot failures in pipelines, jobs, and ML workflows using Azure Monitor, Log Analytics, and custom alerting. DevOps & Automation Maintain CI/CD pipelines using Azure DevOps, GitHub Actions, etc., for ADF, Databricks … ML model deployments. Develop automation scripts in Python, PowerShell, or Bash to reduce manual intervention and improve service reliability. SQL and Data Warehouse Operations Write, optimize, and troubleshoot SQL queries for data validation, root cause analysis, and report troubleshooting. Support and maintain data warehouse environments such as Azure Synapse Analytics ...

move from reactive, siloed systems to intelligent, autonomous and physically aware value chains. You will bring deep expertise in applied AI spanning data engineering, machine learning, computer vision, robotics integration or industrial IoT, combined with a strong appreciation for how physical supply chain, manufacturing and operations environments work. … procurement or operational processes, sufficient to engage credibly with operational leaders and translate AI capability into value. Technical depth: Experience with modern AI and ML platforms such as PyTorch, TensorFlow, Azure ML and AWS SageMaker, data architecture and pipelines, and integration with OT, SCADA or ERP systems. Familiarity with NVIDIA ...

About our Team Data Science Life Sciences is a diverse team focusing on GenAI, ML, NLP. We mainly develop best-in-class enrichment pipelines for Elsevier’s life science .com products such as Reaxys, Embase and Pharmapendium. About the Role As Principal Data Scientist (AI/NLP & Generative … decisions. Hands on involvement will be occasional, on high priority or high risk work. Responsibilities Set technical direction and architectural standards for AI/ML, NLP, and generative AI solutions across teams, including LLM powered workflows, RAG systems, retrieval, ranking, and recommendation capabilities. Guide how scientific metadata, ontologies, taxonomies ...

expert in at least one hyperscaler cloud (such as AWS, Azure, or Google Cloud), you bring authoritative knowledge of that platform's AI/ML services, accelerators, networking, and cost levers, and apply it to deliver best-in-class solutions. Beyond design, you set technical direction and standards, lead … mentor engineers and architects, partner with clients and stakeholders to shape the infrastructure roadmap, and are ultimately accountable for delivering AI/ML infrastructure that meets business SLAs, controls cost, and scales to enterprise and frontier workloads. THE WORK Own the end-to-end architecture and design of optimized compute ...

network to the next level. This is a leader-of-leaders role: a global team of 90+ data scientists, operations research scientists, ML engineers and MLOps engineers, led through managers and principal data scientists. You will set the DS/OR/ML agenda, not just run it. The challenge … policy testing Set the bar for validation before launch: backtesting and simulation frameworks that gate models going into production Own the quality of the ML platform and MLOps standards: feature and model pipelines, CI/CD, online and batch serving, monitoring and drift detection Drive experimentation and causal inference ...

formal training. What you will do Position CDW Custom AI capabilities - Engage with all Customer AI personas: technical personas (CDO, MLOps, AI/ML Engineer, Data Scientist), outcome-driven personas (CEO, CTO, LOB) and ITDMs (CIO, VP Infrastructure). Technical SME on CDW Custom AI – Maintain expert level knowledge … essential Evidence of judgement-based decision-making trade-offs made, unsuitable approaches rejected, risks identified and commercially credible solutions developed. Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn) Experience with cloud platforms (e.g., Azure AI, AWS SageMaker, Google Cloud AI) Experience with Private cloud platforms (HPE Private ...

Data UK practice, within the AI & Modelling Craft: a community of data scientists, AI engineers, modellers and solution architects focused on applying AI, machine learning and advanced analytics to solve customer and growth challenges. Our teams work across the full lifecycle, from identifying opportunities and designing solutions through … understand how prompting, context management, retrieval, tool use, orchestration, evaluation and guardrails work in practice. AI and data science foundations. Good understanding of machine learning and data science alongside generative AI. You know how models are trained, evaluated and monitored, and you can tell when a problem calls ...

visualisation choices without building dashboards or reports. AI-Ready Data - Make sure data platforms provide the quality, context, access and governance that AI and machine learning need. Work with the Principal AI Architect on AI use cases that depend on the data foundation. Security, Observability & FinOps by Design … technical relationships with data platform partners to stay current on roadmaps and capabilities, while using that knowledge as an independent advisor to customers. Continuous Learning & Accreditation - Keep expert knowledge up to date through hands‐on lab time, research and training. Maintain relevant accreditations in at least one core data ...

collaborating closely with architects, AI specialists and client stakeholders. You'll play a key role in delivering production-grade data platforms that support analytics, machine learning and AI workloads across industries including financial services, retail and the public sector. Alongside client delivery, you'll contribute to engineering best … scalability and cost efficiency. Collaborate with architects and customers to translate business requirements into production-ready data solutions. Support integration with downstream analytics, BI, machine learning and AI workloads. Contribute to CI/CD pipelines, deployment automation and infrastructure-as-code practices. What We're Looking For Essential ...

consultancy to find a Data Science Consultant with a strong focus on the Energy & Utilities sectors. In this role, you will apply advanced analytics, machine learning, and mathematical modeling to help major energy providers, utility networks, and public sector organizations drive evidence based decision making. You will work … analytics lifecyclefrom problem formulation and exploratory data analysis to model design, testing, and deployment. Sector-Focused Problem Solving: Apply advanced analytics, OR models, and machine learning to address critical challenges across energy, utilities, and sustainability. Client Translation: Translate complex business requirements into analytical solutions, effectively communicating technical methodologies ...

diagrams to explain a data processing system on a cloud environment. Some knowledge of how you would present diagrams (C4, UML, etc.) Enthusiasm for learning and self‐development. You have experience of working on agile delivery‐lead projects and can apply agile practices such as Scrum, XP, Kanban. … engineers using a variety of techniques such as pair programming or mob programming. Working with data scientists to productionise advanced data deliverables, such as machine learning models Working knowledge of statistics Working with multidisciplinary digital and technology teams Working within the public sector Working with data scientists ...

prioritize high-value quantitative and analytical opportunities. Translate ambiguous investment challenges into actionable technical opportunities, selecting the right approach across software, data, AI, machine learning, optimization, and human judgment. Personally architect, build, and validate solutions from rapid experimentation through MVP and initial production deployment. Lead the productionalisation … work effectively alongside quantitative researchers and distinguish research questions from implementation decisions. Hands-on expertise across Python, data engineering, cloud platforms, AI and machine learning, optimization, and modern application development. Experience architecting AI-enabled and agentic solutions, including RAG, tool use, human-in-the-loop controls, and evaluation ...

science that delivers tangible business results. The Role Reporting to the CTO, you'll own end-to-end data science projects, developing and deploying machine learning models that drive better customer and commercial outcomes. You'll work across: Customer segmentation, propensity modelling and churn prediction. Revenue and demand … environment. Strong Python, SQL and Jupyter skills. Hands-on experience with customer segmentation, propensity modelling and forecasting. Proven ability to deliver end-to-end machine learning projects, from analysis through to deployment. Commercially minded, with strong communication and stakeholder management skills. Experience in a consultancy or agency environment ...

data assets, ensuring they meet the requirements of scalable, transformation-facing analytics. Contribute to data assets and pipelines that are structured to support AI, machine learning, and agentic workflows - increasingly the default mode of delivery rather than a side project. Monitor and optimise the performance of data models … with data pipeline concepts, transformation logic, and ETL/ELT processes. Working understanding of how data assets are structured and prepared to support AI, machine learning, and agentic use cases - this is now a core expectation of the role, not a nice-to-have. Comfortable translating business logic ...

direction Working with cloud-native infrastructure and containerised applications Applying strong analytical and mathematical thinking to engineering problems Working alongside teams using AI and machine learning within the wider technology environment Technology Python | Java | GCP | Docker | REST APIs | Distributed Systems | Event-Driven Architecture | Streaming | Real-Time Data | AI/ML What We're Looking For We're looking for engineers with strong backend foundations who have worked on technically demanding systems where speed, reliability ...

teams to deliver and support secure and scalable AI solutions. Read on for more details... Experience required: Practical experience building and deploying AI or machine learning solutions into production, rather than purely academic, research or proof-of-concept experience. Strong Microsoft AI technology experience, particularly Copilot Studio, Azure … Azure DevOps. Software development capability, ideally with Python; JavaScript or other development languages would be an advantage. A solid technical understanding of LLMs, NLP, machine learning fundamentals and prompt engineering. Experience designing prompts, grounding strategies and knowledge integrations to produce reliable AI responses. An ability to work with ...

connecting proposed solutions to measurable business outcomes. AI-Led Solution Design & Demonstration Design and position AI-led solutions across data platform modernisation, AI/ML, generative AI, agentic workflows and intelligent automation. Develop rapid prototypes, proof-of-concepts and demonstrations using GenAI platforms, low-code/no-code tools … market messaging and strengthen market positioning. Thought Leadership & Market Positioning Maintain a strong understanding of developments in AI, generative AI, agentic AI, machine learning and automation. Apply knowledge of modern cloud data ecosystems, including Google Cloud Platform (GCP), Microsoft Azure, Databricks and Snowflake. Contribute to client workshops, points ...

clusters and distributed training environments, deploy AI systems and models into production, and build and optimize data pipelines that feed AI and ML workflows. You optimize the computational stack for performance, cost, power, and scalability, monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring … EDUCATION Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field BASIC (REQUIRED) QUALIFICATION Experience coding, building, monitoring, troubleshooting applications of AI/ML models; selecting, designing and infrastructure for deploying and running them on premise or on public cloud. Working knowledge of supply chain and engineering processes, such ...

compliant with relevant regulatory requirements. - Support user adoption, testing, documentation and continuous improvement initiatives. Required experience: - Proven experience building and deploying AI or machine learning solutions into production environments. - Strong experience with the Microsoft AI ecosystem, including: Copilot Studio, Azure AI Foundry and Azure DevOps - Software development experience … ideally using Python. - Knowledge of JavaScript or other development languages would be advantageous. - Strong understanding of: Large Language Models (LLMs), Natural Language Processing (NLP), Machine Learning fundamentals and Prompt engineering. - Experience designing prompts, grounding strategies and knowledge integrations. - Experience working with agentic AI and integrating AI capabilities into ...

compliant with relevant regulatory requirements. - Support user adoption, testing, documentation and continuous improvement initiatives. Required experience: - Proven experience building and deploying AI or machine learning solutions into production environments. - Strong experience with the Microsoft AI ecosystem, including: Copilot Studio, Azure AI Foundry and Azure DevOps - Software development experience … ideally using Python. - Knowledge of JavaScript or other development languages would be advantageous. - Strong understanding of: Large Language Models (LLMs), Natural Language Processing (NLP), Machine Learning fundamentals and Prompt engineering. - Experience designing prompts, grounding strategies and knowledge integrations. - Experience working with agentic AI and integrating AI capabilities into ...

semantic models, self-service analytics enablement and reporting rationalisation. Applied AI – Microsoft 365 Copilot, Copilot Studio and agents, Azure AI/OpenAI services, machine learning use‐case discovery, pilot and scale programmes. AI governance and responsible AI – policy, guardrails, risk, and alignment to UK regulatory expectations (UK GDPR … agentic AI. Knowledge of applied AI and its commercial application – Microsoft 365 Copilot, Copilot Studio/agents, Azure AI and OpenAI services, and machine learning use cases. Working knowledge of the UK regulatory and compliance environment as it applies to data and AI – UK GDPR and the Data ...

data governance and privacy; transparency and explainability; and accountability. A key aspect of the role is translating these principles into practical guidance, governance frameworks, learning programmes and deployment standards, helping build a shared understanding of what Responsible AI means for AVEVA, our customers and our stakeholders. Responsible deployment … processes that product and engineering teams can adopt. You will partner closely with our Product, Enterprise IT, Security, Sustainability, Legal, Risk and Talent and Learning teams, while remaining accountable for responsible deployment in policy design, stakeholder engagement, and enterprise learning enablement. Key Responsibilities Responsible AI Governance, Operationalisation & Enablement ...

leading research institution is seeking a Research Fellow in Machine Learning and Spatial Statistics. This role involves high-quality research under notable supervision, focusing on Bayesian models and spatial statistics. Located at The Alan Turing Institute in London, the position offers a salary ...

revolutionary idea. Rather than move the data from the source, move the algorithms to the data to learn at the data source. This learning can be achieved with the intelligence of many devices through novel federated AI technology. OctaiPipe is developing the AI for Cooling Efficiency (ACE) application … documented relationships are wrong. Develop causal models that transfer between heterogeneous sites, in close collaboration with the team's reinforcement learning scientists and ML systems engineers. Take methods from prototype to production with the ML systems engineers: evaluation on historical replay, staged rollout and monitoring for drift. Raise ...

full-time permanent Lecturer/Senior Lecturer (equiv Assistant/Associate Professor) in Artificial Intelligence/Machine Learning/Data Science/Statistics at Imperial College London Oct 30, 2023 I am excited to announce that Imperial College London invites applications for one full-time permanent faculty member … College’s Imperial-X (I-X) initiative. Imperial-X brings academia and industry together to tackle the world’s greatest challenges using machine learning, and throwing out the rule book for the university. Imperial College London is a world-leading university whose reputation for excellence in research ...