351 to 375 of 3,468 Machine Learning Jobs in London

wide goals Drive architectural decisions and technical excellence across teams Ensure robust systems for deployment, monitoring, scalability, and reliability of infrastructure supporting AI/ML workloads Collaborate with engineering leaders globally to align on technical standards, best practices, and cross-site initiatives Team Building & Talent: Build and scale high-performing … office Deep technical expertise in distributed systems, cloud platforms (AWS, GCP, or Azure), and infrastructure automation Experience with infrastructure supporting large-scale AI/ML systems, including: GPU infrastructure and orchestration ML training and inference pipelines Model serving and deployment at scale Strong understanding of modern infrastructure technologies: Kubernetes, Terraform ...

team player and work collaboratively with the business and the team.Connect to your skills and professional experience Essential:Experience full-stack software or ML engineering with strong Python Development practices (CI/CD, testing, muti-threading/asynchronous execution, PEP8 etc.)Experience designing, testing and deploying production-scale AI/… ML solutions (APIs, microservices, cloud deployment, endpoint deployment, registries, pipelines)Experience with LLM based applications (NLP, embeddings, semantic search, RAG, Graph RAG, fine-tuning)Proficient in ML tools and modern ecosystem (PyTorch, MLFlow, FastAPI, SQL Alchemy)Experience retrieving data from structured, unstructured and web sources (Selenium/PlayWright, Pandas ...

real-world problems in finance. For nearly two decades, we have led our industry and worked at the frontier of applying AI/ML to investment management. We have become a multibillion-dollar asset manager, and we have ambitious goals for the future.Your colleagues will include internationally recognized experts … skills, and the ability to collaborate effectively with researchers whose primary expertise is not software engineeringPreferred QualificationsExperience with experiment management, model evaluation pipelines, or ML workflow orchestrationFamiliarity with modern ML/AI infrastructure patterns (model serving, feature stores, distributed training)Experience with performance profiling and optimization of numerical or modeling ...

apply AI to transform the way biomedical professionals carry out their research and day-to-day exploration.What you’ll be doing· Design and implement ML/AI solutions end-to-end, from the idea and data exploration phase to deployment and monitoring, balancing cutting-edge techniques with pragmatism to deliver … reliable, maintainable, production-ready and can be readily adapted to future developments.· Choose the right approach for the problem at hand, evaluating classical ML and NLP techniques, LLM-based solutions, and agentic solutions to balance trade-offs between speed, cost, complexity, interpretability, and performance.· Collaborate closely with product, design ...

strategy for your accounts, from initial discovery through production deployment and consumption growthLead complex architecture discussions — designing scalable, production-grade solutions spanning data engineering, ML/AI, and real-time analyticsServe as a trusted technical advisor to customer architects, engineering leads, and DirectorsDrive technical wins in competitive scenarios by demonstrating … 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)Proven ability to lead architecture discussions with senior technical stakeholders — whiteboarding, design reviews, and trade-off analysisExperience with production deployments on public ...

example Python, C#, JavaScript, Kotlin, Java or Go)Experience with Gen AI tools (such as LangGraph, CrewAI, Hugging Face or OpenAI APIs) and ML frameworks like PyTorch or TensorFlowStrong understanding of ML fundamentals (training, evaluation, deployment) and LLM-specific challenges such as prompt engineering, latency and cost optimisationExperience with cloud ...

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 integration Transformational Impact: Guide strategic customers as they implement transformational big data projects including end‐to‐end design, build and deployment … with distributed computing with Apache SparkTM and knowledge of Spark runtime internals Familiarity with CI/CD for production deployments Working knowledge of MLOps, ML/AI models and AI APIs Design and deployment of performant production end-to-end data architectures and applications that combine data pipelines, ML/ ...

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/ ...

real-world problems in finance. For nearly two decades, we have led our industry and worked at the frontier of applying AI/ML to investment management. We have become a multibillion-dollar asset manager, and we have ambitious goals for the future. Your colleagues will include internationally recognized experts … ability to collaborate effectively with researchers whose primary expertise is not software engineering Preferred Qualifications Experience with experiment management, model evaluation pipelines, or ML workflow orchestration Familiarity with modern ML/AI infrastructure patterns (model serving, feature stores, distributed training) Experience with performance profiling and optimization of numerical or modeling ...

architectures and orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool‐use patterns, human‐in‐the‐loop workflows, and agentic safety at enterprise scale. AI/ML Engineering & MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using … such as MLflow, Vertex AI, or SageMaker. Cloud AI Platforms: Demonstrated deployment of AI workloads on AWS, GCP, and/or Azure including managed ML services, scalable inference infrastructure, and vector stores. Programming & Frameworks: Strong Python proficiency; working knowledge of PyTorch or TensorFlow; applied experience with AI application frameworks (LangChain ...

architectures and orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool-use patterns, human-in-the-loop workflows, and agentic safety at enterprise scale* AI/ML Engineering & MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using … such as MLflow, Vertex AI, or SageMaker* Cloud AI Platforms: Demonstrated deployment of AI workloads on AWS, GCP, and/or Azure including managed ML services, scalable inference infrastructure, and vector stores* Programming & Frameworks: Strong Python proficiency; working knowledge of PyTorch or TensorFlow; applied experience with AI application frameworks (LangChain ...

agent architectures and orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool-use patterns, human-in-the-loop workflows, and agentic safety at enterprise scaleAI/ML Engineering & MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using … platforms such as MLflow, Vertex AI, or SageMakerCloud AI Platforms: Demonstrated deployment of AI workloads on AWS, GCP, and/or Azure including managed ML services, scalable inference infrastructure, and vector storesProgramming & Frameworks: Strong Python proficiency; working knowledge of PyTorch or TensorFlow; applied experience with AI application frameworks (LangChain, LlamaIndex ...

lifecycle.Drive architectural governance, ensuring traceability, documentation accuracy, and compliance.Ensure solutions meet compliance standards (e.g. ISO 27001).AI & Data AwarenessEnsure solution designs support AI/ML-enabled capabilities where relevant (e.g. data lineage, governance, feature management, and model integration patterns).Collaborate with wider technical teams to define integration approaches for predictive … frameworks (e.g. TOGAF, Zachman).Background in software engineering, data architecture, or infrastructure engineering.Exposure to designing internal accelerators, frameworks, or reusable components.Awareness of AI/ML architecture patterns, generative AI integration considerations, or data lineage and monitoring concepts.CompanyRapidly growing financial services organisation with offices in LondonHybrid working Salary & BenefitsThe salary range ...

testing to guarantee system stability under high concurrent traffic. Required Skills and Qualifications Core Programming & Frameworks Language: Mastery of Python and its asynchronous ecosystem. ML Ecosystem: Deep expertise in PyTorch, Scikit-learn, and NumPy. Compilation: Experience accelerating Python code via Numba or Triton. Conversational AI Experience Speech Technologies: Hands … Infrastructure & Operations Containers: Advanced knowledge of Docker, Podman, and container orchestration. Cloud Providers: Practical experience managing AI workloads on AWS (EC2, SageMaker), Azure (Azure ML), and GCP (Vertex AI). CI/CD Tools: Experience with GitLab CI, GitHub Actions, Jenkins, or specialized MLOps platforms (e.g., Kubeflow, MLflow). Preferred ...

agile team that designs, builds, and operates the services that make large language models usable at scale. This is an infrastructure-meets-ML role: you don't need to be an ML researcher, but you should be excited to learn how model architectures and inference constraints translate into real production ...

agile team that designs, builds, and operates the services that make large language models usable at scale. This is an infrastructure-meets-ML role: you don't need to be an ML researcher, but you should be excited to learn how model architectures and inference constraints translate into real production ...

technical leadership on architecture decisions, system design, technology selection, and engineering standards across multiple initiatives.Mentor and develop engineers in engineering, system design, AI/ML technologies, and modern development practices.Evaluate emerging technologies and recommend adoption where they improve product capability, engineering efficiency, or client experience.Foster a culture of innovation, experimentation … DevSecOps, automated testing, observability, and secure software development.Strong programming skills in Python, with familiarity in C++, C#, or similar languages.Experience with AI/ML technologies and hands-on knowledge of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, prompt engineering, or similar Generative AI technologies is highly desirable.Familiarity ...

leadership on architecture decisions, system design, technology selection, and engineering standards across multiple initiatives. Mentor and develop engineers in engineering, system design, AI/ML technologies, and modern development practices. Evaluate emerging technologies and recommend adoption where they improve product capability, engineering efficiency, or client experience. Foster a culture … automated testing, observability, and secure software development. Strong programming skills in Python, with familiarity in C++, C#, or similar languages. Experience with AI/ML technologies and hands-on knowledge of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, prompt engineering, or similar Generative AI technologies is highly ...

full-stack shop consisting of product and portfolio leadership, data engineering, infrastructure and DevOps, data/metadata/knowledge platforms, and AI/ML and analysis platforms, all geared toward: Building a next-generation, metadata- and automation-driven data experience for GSK’s scientists, engineers, and decision-makers, increasing productivity … reducing time spent on “data mechanics” Providing best-in-class AI/ML and data analysis environments to accelerate our predictive capabilities and attract top-tier talent Aggressively engineering our data at scale, as one unified asset, to unlock the value of our unique collection of data and predictions ...

technical blog posts QualificationsRequired 8–12+ years engineering experience; 3+ years in customer-facing or FDE roles Proven track record deploying AI/ML or data-intensive applications in production Strong full-stack skills: Python, Node.js/Go, React/Vue, SQL, REST/GraphQL DevOps proficiency: Docker, Kubernetes ...

containers (Docker, Container Orchestration like Kubernetes/ECS/GKE). A genuine interest in and at least foundational experience with AI/ML concepts and technologies, demonstrating an eagerness to grow into a specialised AI Engineering role. Proven track record of delivering high‐quality work and driving forward best ...

however candidates must be within commutable distance of the London office for occasional onsite visits). This is a highly hands‐on, AI/ML-enabled engineering position focused on building the platforms and automation that power modern software and AI workloads across the firm. The successful candidate must ...

join our team in London, London, City of (GB-LND), United Kingdom (GB).Key Responsibilities• Design, optimize, and maintain scalable Databricks pipelines (ETL, streaming, ML workflows).• Perform cluster and job performance tuning: optimize cluster sizing, caching, partitioning, and shuffle management.• Monitor Spark job metrics, analyze logs, and identify bottlenecks ...

operational readiness: testing strategy, CI/CD, release notes, production deployment governance, and incident/root-cause management.Identify and deliver pragmatic AI/ML opportunities where valuable (e.g., agentic workflows, RAG/LLM-enabled capabilities) with appropriate governance and human-in-the-loop controls. What you need to have:Experience ...

ensure integration strategies align with internal APIs, data lakes, and cloud-based platforms (AWS, Azure, GCP).Continuously grow and develop skills across AI/ML and GenAI and increase business acumenSupport client onboarding, vendor integrations, and managed service engagements by providing technical specifications and solution architecture for data flows.Technical proficiency ...