1,151 to 1,175 of 1,334 Machine Learning Jobs in England

pipelines for real-time and batch data ingestion from diverse and complex sources. Model and optimise data for performance, visibility, and downstream analytics/ML use cases. Champion best practices: CI/CD, TDD, GitOps, observability, schema validation, and data quality. Collaborate with data scientists and ML engineers to deploy … production-grade AI/ML systems. Guide clients on data strategy, architecture, and platform modernisation. Mentor and influence engineers across Sahaj, helping grow our collective data engineering capability. What we’re looking for Must have solid experience as a Senior/Lead Data Engineer in complex enterprise environments. Can demonstrate ...

PowerBI, Tableau, Looker,... Background in Data Governance and Data Catalog solutions: Axon, Informatica EDC, Colibra, Purview, etc. Previous experience in Artificial Intelligence techniques: ML/Deep Learning, Computer Vision, NLP, etc General information: Start Date: ASAP Length of Contract: 1 year (minimum) Work Location: Madrid Remote working. ...

intersection of three businesses: (1) a private equity investment arm, (2) an in-house AI transformation firm that designs and deploys bespoke GenAI, ML, and automation solutions, and (3) a suite of proprietary software products. Our team comes from places like Google, Palantir, Meta, Surge AI, Blackstone, TPG, Cerberus, Millenium … end. About You: You are a hybrid: part technologist, part strategist, part operator. You are equally comfortable whiteboarding a model architecture with an ML engineer, presenting an AI transformation roadmap to a private equity partner, and pressure-testing an LBO model. You move fast, you think in structures ...

high-impact defence programmes. They're building teams across application (C#, Java, Python), low-level/embedded (C/C++, Rust), Data, AI/ML, and DevOps skillsets. They are deliberately setting a very high bar, hiring engineers from top universities who are among the strongest technically in their field. … balancing delivery pressures Technical Lead Requirements: Strong software engineering background with leadership experience Experience in Application, or Low-Level/Embedded, or AI/ML, or DevOps, or Data Tech: e.g. C#, Python, C++, Rust, Typescript, C, Java, AI/ML tools, LLMs/RAG, SQL, multi-cloud etc... Proven ...

programmes — where the stakes are real, and the impact is measurable. What you'll do Architect end-to-end AI systems (GenAI and traditional ML) built for production, not the lab Own the full technical lifecycle: model selection, deployment, MLOps, evaluation, and monitoring Deploy across cloud platforms (AWS Bedrock, Azure … senior stakeholders — and know when AI isn't the right answer What we're looking for Proven track record delivering production AI/ML and GenAI at enterprise scale — real impact, not just prototypes Expert Python with hands-on experience in PyTorch, TensorFlow, LangChain, LangGraph, RAG pipelines, and LLMs Solid ...

variety of open-source, commercial, and bespoke solutions. Leadership & Mentorship: Lead a team of Quality Engineers, providing technical advice and supporting their professional learning and development. Technical Authority: Provide expert guidance on test strategies, tools, and architecture, forming close relationships with engineering leads and stakeholders. QA Health Checks: Perform …/CD, release management, and pipeline implementation (Jenkins, Azure DevOps, GitHub Actions). Bonus Points for: Experience with emerging technologies, e.g using AI/ML within your engineering projects. Familiarity with testing in cloud environments such as AWS, Azure or Google Cloud . Industry-recognised certifications in programming languages ...

Title: AI Engineer Location: London, UK Job Description 2–4 years of IT experience, ideally with exposure to AI/ML concepts. Understanding of: Generative AI and Agentic AI AI product workflows and how AI components integrate into domain-specific use cases Retrieval-Augmented Generation (RAG) fundamentals and context engineering … change. We also empower the business with agile digital at scale to deliver unprecedented levels of performance and customer delight. Our always-on learning agenda drives their continuous improvement through building and transferring digital skills, expertise and ideas from our innovation ecosystem. Visit www.infosys.com to see how Infosys ...

permanent basis. In order to be successful, you will have the following experience: Strong background within leading end to end implementation of AI/ML and Generative AI solutions Experience within pre-sales, sales or consulting Broad experience across major cloud platforms (AWS, Azure, GCP) with Generative AI services Experience … data scientists and engineers, fostering a culture of innovation and engineering excellence. Design scalable, secure, and production-ready architecture that integrate LLMs and ML models into complex enterprise workflows. Oversee the full technical lifecycle, from selecting the right model architecture to ensuring robust CI/CD and MLOps pipelines. Deploy ...

position will suit an architect who can move comfortably between traditional enterprise data approaches and modern patterns such as lakehouse, streaming, data mesh and ML-enabled platforms - and who values clarity, governance and technical rigour. Candidates must hold active SC clearance . What experience you'll bring: Leadership: Recent experience … NoSQL technologies. Understanding of ETL/ELT design trade-offs. Awareness of DevOps practices and CI/CD for data platforms. Understanding of how ML/AI workloads integrate into data architectures. Hands-on coding ability (e.g. Python, Java, R) is beneficial but not required. Governance, Security and Data Management ...

position will suit an architect who can move comfortably between traditional enterprise data approaches and modern patterns such as lakehouse, streaming, data mesh and ML-enabled platforms - and who values clarity, governance and technical rigour. Candidates must hold activeSC clearance. What experience you'll bring: Leadership: Recent experience … NoSQL technologies. Understanding of ETL/ELT design trade-offs. Awareness of DevOps practices and CI/CD for data platforms. Understanding of how ML/AI workloads integrate into data architectures. Hands-on coding ability (e.g. Python, Java, R) is beneficial but not required. Governance, Security and Data Management ...

Ownership & Delivery Excellence Own end-to-end delivery for a portfolio of data/AI programs (data platforms, lakehouse, MDM, analytics, GenAI/RAG, ML ops), ensuring on-time, on-budget outcomes with high CSAT. Establish governance cadence : steerco, RAID logs, budget/burn tracking, change control, risk mitigation, quality … leading multi-workstream programs or portfolios. Proven success delivering across data platforms/lakehouse, ETL/ELT (e.g., Spark, dbt), MDM, BI/analytics, ML/GenAI (RAG, LLMOps) . Strong familiarity with Snowflake, Databricks, Azure/AWS/GCP data services, Kafka, Delta/Iceberg, Power BI/Tableau ...

into technical specs, and establish clear KPIs. Data Architecture: Design robust pipelines for data collection, cleaning, and preprocessing to ensure high-quality inputs for ML models. Model Development: Select and train appropriate architectures (BERT, GPT, etc.) using supervised, unsupervised, or reinforcement learning strategies. Prompt Engineering: Design, test, and iterate … Monitoring: Collaborate with engineers to push models into production and establish automated monitoring systems to track drift and performance. Technical Requirements Core AI/ML: Strong experience in ML algorithms, LLM architectures, and deep learning frameworks. Generative AI: Proven expertise in Prompt Engineering and fine-tuning pre-trained models. ...

into technical specs, and establish clear KPIs. Data Architecture: Design robust pipelines for data collection, cleaning, and preprocessing to ensure high-quality inputs for ML models. Model Development: Select and train appropriate architectures (BERT, GPT, etc.) using supervised, unsupervised, or reinforcement learning strategies. Prompt Engineering: Design, test, and iterate … Monitoring: Collaborate with engineers to push models into production and establish automated monitoring systems to track drift and performance. Technical Requirements Core AI/ML: Strong experience in ML algorithms, LLM architectures, and deep learning frameworks. Generative AI: Proven expertise in Prompt Engineering and fine-tuning pre-trained models. ...

Cloud & AWS Integration Strong understanding of AWS IAM, S3‐based data lake architectures, and PrivateLink or equivalent patterns for secure cross‐cloud connectivity. AI & ML Enablement Practical experience designing data infrastructure to support AI/ML workloads, including vector stores, embedding pipelines, and integration with RAG‐based systems. Stakeholder & Communication ...

access to trusted, timely, and well-modelled data to support decision-making, operational reporting, and the foundations for advanced analytics and future AI/ML capabilities. Key Accountabilities & Responsibilities Snowflake Data Engineering Delivery Design, build, and maintain high-quality data pipelines and models in Snowflake to support business analytics … Snowflake datasets. Enablement for Analytics & Data Science Implement dimensional models that provide clean, well-structured, reusable datasets for reporting, scenario modelling, and emerging ML/AI use cases. Monitoring, Reliability & Operations Implement and maintain monitoring, alerting, logging, and cost-management processes for Snowflake and data pipelines to ensure a stable ...

quickly together, embracing collaboration and change as part of start‐up life. Individuals thrive here if they enjoy taking ownership, wearing multiple hats and learning quickly, and are comfortable going beyond core hours when work demands it – not because it is expected every day, but because seeing progress matters. … Integrate performance testing techniques across multiple types of simulation (e.g. Gazebo, rosbag‐based workflows). Collaborate with hardware engineers, systems architects and AI/ML specialists to integrate embedded software and test frameworks into the overall system. Participate in code reviews, performance optimisation and comprehensive testing (unit, integration and system ...

technology with strategic business outcomes. Implement monitoring, observability, and disaster recovery strategies ensuring high availability, system resilience, and proactive issue resolution. Lead AI/ML and analytics integrations with Databricks, Power BI, and MDM platforms to enable advanced reporting and insights. Mentor and enable internal teams through technical training, knowledge … data Data Model, Data Mapping, ETL Mapping Data Governance (Purview, Databrick Unity Catlog) Data Profiling, Data Quality, Security MDM (Profisee, Informatica) HADR, AI/ML Compute, network strategies (Private Endpoint, Vnet, ExpressRoute) Security frameworks, IAM, RBAC, firewall rules, Zero Trust architecture Threat modelling, risk assessments Monitoring, Logging Cost and performance ...

Identify performance or reliability bottlenecks and propose improvements. Required Qualifications & Experience Strong experience with Python and Django. Familiarity with Torch (PyTorch) for analytical or ML workloads. Proficiency in SGLang or willingness to learn domain-specific languages. A keen sense of system design and desire to build elegant, maintainable solutions. General … time streaming architectures. Familiarity with cloud platforms (AWS, GCP, Azure) and container orchestration (Kubernetes/Docker). Exposure to data pipelines, analytics workflows, or ML deployment. Experience working in startup or product-led teams. Why Zuma Labs? Work in a high-impact startup environment where your work immediately affects ...

performance cloud workflows. Raising engineering standards – introducing Git, CI/CD pipelines, automated testing and best-practice engineering processes. Preparing data for AI and ML – designing resilient architectures that ensure high-quality, accessible data for future AI initiatives. What You Will Need In The Role Of Data Engineer 3–5+ … Python for automation Solid understanding of star schemas and Kimball methodologies Experience supporting advanced Power BI models (DAX and Power Query) Exposure to ML/AI concepts (e.g. scikit-learn, TensorFlow) or an interest in preparing data for LLMs, RAG architectures or predictive models Experience with Docker and/ ...

Who Are We? Taking care of our customers, our communities and each other. That’s the Travelers Promise. By honoring this commitment, we have maintained our reputation as one of the best property casualty insurers ...

Lead Deep Learning Researcher – Hedge Fund I’m partnered with an industry-leading Hedge Fund that, after nearly 20 years of pioneering innovation and hiring some of the brightest minds in the market, is now looking to further its success through the latest advancements in deep learning . … NeurIPS, ICML, or ICLR. Hands-on experience applying deep learning models to complex, ideally temporal or financial, datasets. Expertise in Python and modern ML frameworks (TensorFlow, PyTorch, JAX, etc.). Prior experience in a commercial or industry setting post-PhD, ideally in a similarly data-driven domain. Why Apply ...

focused on helping clients eliminate wasteful and fraudulent ad traffic. The Role We are looking for an experienced Data Scientist with strong ML & NLP expertise to build and improve models that detect invalid traffic (IVT) and protect ad spend. You’ll work with large-scale data, design production-ready ML … systems, and collaborate closely with engineering to keep models performant and reliable. Responsibilities Develop and deploy ML models to detect bot and fraudulent traffic Lead research and prototyping of new detection methods Monitor model performance and address drift or false positives Collaborate across teams to turn insights into product features ...

physical robot hardware Mentor junior researchers and engineers, and publish at top-tier venues What We're Looking For Essential: PhD/MSc in ML, Robotics, CS, or related field with 4+ years of equivalent industry research experience Demonstrated expertise training and deploying learned manipulation policies on real robots Strong … tier venues — conference attendance and open-source contributions are actively supported What's On Offer Join a team with world class applied research scientists, ML engineers, and robotics software engineers A mature platform that ships to physical hardware, not slides Active support for conference attendance and open-source contributions Competitive ...

engineering workflow. Rapid Iteration: Comfort operating at extreme velocity in ambiguous environments. You know intuitively when to hack together a messy prototype for learning versus when to build robustly for production scale. Collaborative Mindset: Strong stakeholder communication skills. You can naturally gather requirements from non-technical users and explain … intensive domains, or high-volume publishing. Infrastructure Expertise: Deep understanding of modern cloud infrastructure, including containerisation, serverless architectures, and Infrastructure as Code (IaC). ML Intuition: A background in data science or ML engineering that provides intuition for embedding spaces, attention mechanisms, and evaluation methodologies. Production Observability: Experience with monitoring ...

fast-paced, evolving environments. Desirable Proficiency in SQL for data extraction and transformation. Knowledge of supply chain/logistics KPIs. Familiarity with AI/ML tools and languages, including Python, R, Azure ML, or AWS SageMaker. Why Ligentia? Why now? Accelerated growth in one of the most dynamic industries ...