251 to 275 of 3,264 Machine Learning Jobs in the UK

Advanced knowledge of software applications and technical processes with considerable in‐depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.) Ability to tackle design and functionality problems independently with little to no oversight Experience in Computer Science, Computer Engineering, Mathematics ...

line of code, but you understand the platform end-to-end and own outcomes. You'll partner closely with product owners, data scientists and machine learning engineers, site reliability engineering, and business stakeholders to deliver stable, scalable cloud-native analytics. Job responsibilities Own application governance and records ...

infrastructure automation Background in enterprise software, SaaS platforms, or technically demanding application domains (scientific computing, real-time systems, large-scale data) Exposure to integrating machine learning, generative AI, or LLM-based components into application features Experience mentoring less experienced engineers Energy industry experience is not required ...

agentic workflows that tie it all together. You will partner with computational chemists, CADD scientists, software engineers, and hardware engineers, and apply AI/ML to build the next generation of Chemify's platform. What sets this role apart is the combination of breadth of ML problems — generative chemistry, vision … search, agents — paired with a robotic platform that turns your models into physical experiments. If working across a wide range of hard ML problems on a real-world platform sounds like the right shape of job for you, we'd love to welcome you to our team. Key Responsibilities Build ...

artefacts (templates, examples, checklists) that support effective adoption of recommended practices.* Lead and facilitate the AI engineering knowledge and community activities—curating content, running learning sessions, and integrating feedback into improved guidance.* Review and adapt industry best practices, working with internal experts to publish reusable patterns and architectural recommendations.B … Experience applying software engineering fundamentals: writing tests, structuring user stories, managing iterative releases, and working with CI/CD pipelines.* Experience in AI/ML, software, or platform engineering, with exposure to automated testing and infrastructure‐as‐code or policy‐as‐code.* Working knowledge of AI observability (logs, metrics, traces ...

Demonstrated experience building and deploying AI, data, software or platform products in enterprise or customer environments. Experience with foundation models, large language models, machine learning models or multimodal AI applications. Familiarity with AI agents, retrieval-augmented generation, embeddings, vector databases, semantic search, model APIs or orchestration frameworks. Understanding ...

infrastructure automation Background in technically complex or scientifically demanding application domains (distributed systems, scientific computing, real-time systems, large-scale data) Experience integrating machine learning, generative AI, or LLM-based components into production systems Experience contributing to open-source projects, or integrating open-source components at scale. Energy ...

fine‐tuning, tailored to diverse product requirements. You will design and build production‐grade inference systems, working across Swift, Java, and Python to integrate ML capabilities seamlessly into Apple's ecosystem. As a senior technical contributor, you will set engineering standards, mentor engineers, and influence the technical roadmap for generative … features, taking full ownership from problem definition and prototyping through production release, working across Swift, Java, and Python codebases Establish engineering excellence across the ML development lifecycle, including robust testing, reproducibility, monitoring, documentation, and CI/CD for model and data pipelines Partner with research, product, design, and platform teams ...

Machine Learning Engineer at Autodesk Research, you will work side-by-side with researchers and engineers to build and enable ML-powered capabilities in Autodesk products. You are an ML engineer with strong software engineering skills who is passionate about solving problems and building things, and excited … offices in London. Responsibilities Collaborate on projects at the intersection of research and product with a diverse, global team Build and iterate on ML pipelines, prototypes, and evaluation workflows, enabling rapid deployment and testing of experimental models Work with complex datasets and representations for model training, evaluation, and analysis Analyze ...

ability to detect financial crime, prevent fraud, and safeguard our customers. Working closely with data scientists and engineers, you will focus on developing scalable ML pipelines, agentic AI systems, production‐grade model code, and robust monitoring systems. You will play a key role across the full machine learning … frameworks (e.g., Spark). Experience with deep learning/NLP frameworks (e.g., PyTorch, Hugging Face). Solid understanding of software engineering principles and ML lifecycle practices (MLOps/LLMOps). Previous experience owning and delivering ML projects, including stakeholder management. Additional Highly Valued Skills Experience leading machine learning ...

beyond proof of concept into secure, production-grade deployment. The role: - Design AI and GenAI architecture strategies and roadmaps - Build scalable AI/ML and Agentic AI platforms - Work across LLMs, RAG, embeddings and semantic search - Support AI deployment, governance and integration patterns - Collaborate with engineering, data, DevOps and business … Vector databases MLOps/LLMOps AWS/Azure/GCP/Databricks Kubernetes & GPU infrastructure What you'll bring: - Experience designing enterprise AI or ML architectures - Strong grasp of cloud and modern data platforms - Exposure to scalable AI deployment and integration patterns - Financial Services or regulated industry experience (essential) - Strong ...

Solution Architects through to senior technical leadership. What you'll be doing Designing AI and GenAI architecture strategies and roadmaps Building scalable AI/ML and Agentic AI platforms Working across LLMs, RAG, embeddings and semantic search Supporting AI deployment, governance and integration patterns Collaborating with engineering, data, DevOps … frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms Exposure to scalable AI deployment and integration patterns Financial Services or regulated industry experience essential Strong ...

full lifecycle from data ingestion through to deployment, monitoring, and optimisation. Responsibilities AI Engineering & Delivery Design, develop, and deploy end‐to‐end AI/ML solutions, including classical ML and generative AI use cases. Build and maintain production‐ready AI pipelines (data ingestion, feature engineering, model training, evaluation, deployment, monitoring … required; additional languages a plus). Ability to communicate complex technical concepts clearly to non‐technical stakeholders. Experience Strong experience as an AI Engineer, ML Engineer, or similar role delivering production AI systems. Experience with ML frameworks (e.g. PyTorch, TensorFlow, scikit‐learn). Proven experience with MLOps and deploying models ...

Solution Architects through to senior technical leadership. What you'll be doing Designing AI and GenAI architecture strategies and roadmaps Building scalable AI/ML and Agentic AI platforms Working across LLMs, RAG, embeddings and semantic search Supporting AI deployment, governance and integration patterns Collaborating with engineering, data, DevOps … frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms Exposure to scalable AI deployment and integration patterns Financial Services or regulated industry experience essential Strong ...

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 ...

frameworks Experience in building AI business, solutions and competency • Experience in GenerativeAI, LLM and prompt engineering Exsentive experience in technology within in AI/ML leadership role Strong hands on experience with ML frameworks Experience with cloud platforms(AWS, AZURE) Deep understanding of ML algorithms, LLMs and AI architecture Proven … adoption Experience in driving AI-first thinking across the organization. Proven experience in leadership and team building: Build, mentor and scale AI/ML/Data Science teams and establish best practices for model development, deployment and governance. Proven ability to foster a culture of experimentation, innovation and continuous learning. ...

Bachelor’s degree in computer science, engineering, mathematics, statistics, or related fields. 10+ years in technology architecture, 3–5 years focused on AI/ML architecture in large, complex enterprise environments. Hands‐on command of modern AI stack: LLM APIs and fine‐tuning, vector databases, RAG, embedding pipelines, prompt engineering … data platforms (Snowflake, Databricks, or comparable) and integrating AI on top of them. Strong understanding of cloud‐native architecture on AWS, including AI/ML services such as Bedrock. Ability to produce high‐quality architecture artifacts—reference architectures, technology radars, ADRs, capability assessments. Familiarity with enterprise architecture frameworks such ...

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 measurable impact. … 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 ...

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/ ...

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 ...

ensuring traceability, documentation accuracy, and compliance. Ensure solutions meet compliance standards (e.g. ISO 27001). AI & Data Awareness Ensure 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 … 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. Company Rapidly growing financial services organisation with offices in London Hybrid working Salary ...

opportunity for you to take your software engineering career to the next level. As a Software Engineer III at JPMorganChase within the AI/ML and Data Platform, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure … algorithms that run synchronously with appropriate systems. Performs platform design, set-up and configuration, providing engineering support to data engineering teams, Data Science/ML, and Application/integration teams. Collaborates with engineering and data teams to optimize infrastructure and deployment processes, focusing on automation and operational excellence. Identifies opportunities ...

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 ...

Preply is scaling an AI-powered learning platform used by millions of learners and tutors worldwide. As ML and GenAI become increasingly central to our product, marketplace, and operational systems, we’re looking for a Staff ML Ops Engineer to define and evolve the platform that enables machine … engineer who can combine deep hands‐on expertise with platform vision. You will help define the current and future direction of Preply’s ML platform, set technical standards, and design the systems that allow ML teams to move from research to production quickly, safely, and cost‐efficiently. You’ll partner ...