351 to 375 of 747 MLOps Jobs in England

deepen your knowledge in this areaA solid understanding of software engineering principles, including data pipelines, CI/CD, containerisation and observability, with exposure to MLOps practices and toolingComfortable providing technical guidance, mentoring and support to a small number of less‐experienced engineersEnjoy collaborating across teams and contributing to shared engineering ...

quantitative discipline, e.g. Computer Science, Mathematics, Statistics Track record of developing, deploying business critical machine learning models Broad knowledge of MLOps tooling – for versioning, reproducibility, observability etc Experience monitoring, maintaining, enhancing existing models over an extended time period Specialism in NLP or Computer Vision Solid understanding of fundamentals of statistics ...

data and AI.Build POVs and architecture options that balance business value, feasibility, and cost.Act as a trusted advisor for our clients across data platforms, MLOps, Generative and Agentic AI, and application design.Thought Leadership, GTM & Vertical ExpertiseRepresent Datatonic on site at client, at events, conferences, partner sessions, and executive dinnersWork with ...

AI.Build POVs and architecture options that balance business value, feasibility, and cost. Act as a trusted advisor for our clients across data platforms, MLOps, Generative and Agentic AI, and application design. Thought Leadership, GTM & Vertical ExpertiseRepresent Datatonic on site at client, at events, conferences, partner sessions, and executive dinnersWork with ...

share knowledge.Experience bringing ML products from ideation through to production.Experience working in fast-paced, product-driven environments.Familiarity with cloud-native ML platforms and MLOps practices.Publications, open-source contributions or evidence of staying current with developments in machine learning and AI.Additional InformationBeneFITS’ Employee discount (hello ASOS discount!) Employee sample sales ...

Computer Science, Engineering, or a related field, or equivalent practical experience.Desirable:Experience with financial services or other regulated industries.Contributions to open-source projects.Experience with MLOps and the deployment of machine learning models.What we’ll provide youThis is a unique role that will put you in the position to be part ...

related field, or equivalent practical experience. Desirable: Experience with financial services or other regulated industries. Contributions to open-source projects. Experience with MLOps and the deployment of machine learning models. What we’ll provide you This is a unique role that will put you in the position to be part ...

facing sales activitiesData governance & compliance - Strong background in security frameworks, regulatory compliance (GDPR), data lineage, and quality managementAI/ML integration capabilities - Experience with MLOps, analytics platforms, and integrating AI/ML into data architecturesAgile delivery & thought leadership - Proven agile/hybrid delivery experience with contribution to practice growth through ...

knowledge of modern AI, cloud, and enterprise technologies, with the ability to rapidly learn, evaluate, and adopt emerging technologies.Experience with containerization, CI/CD, MLOps/LLMOps, observability, and secure-by-design engineering practices.Demonstrated ability to leverage AI-assisted development and intelligent engineering tools to accelerate solution delivery.Excellent consulting, communication ...

generation, environment design, mid-training, supervised fine-tuning, offline reinforcement learning, online reinforcement learning, reward modelling, transition modelling, etc. Our team also has dedicated MLOps, Infra and Inference support at scale. We focus on improving the long horizon, goal-conditioned instruction-following of large models for GUI/Computer ...

Master’s or Ph.D. in a quantitative or technical field (e.g. Mathematics, Statistics, Computer Science). Nice to Have: Familiarity with production environments, MLOps, or data pipelines. Experience working with large-scale data using PySpark or equivalent distributed data processing tools. Experience in gaming, e-commerce, or subscription-based products.Benefits ...

required to prevent recurrence. Deep expertise in Kubernetes, alongside experience operating infrastructure across hybrid on-premises and cloud environments. Experience building, running or evolving MLOps infrastructure, ideally including notebook environments such as Kubeflow, GPU compute for distributed training and model serving. Familiarity with batch compute for data-intensive scientific workloads ...

tools, job control, review) that make ML/automation workflows intuitive and accessible.Collaborate with global stakeholders and architects to implement cost-effective data and MLOps pipelines; document methodologies.Build, package, and configure complex software systems across Linux.Improve efficiency, stability, and observability through profiling, metrics, and tracing.Validate work with methodical testing (unit ...

Azure Data)Data governance & compliance - Strong background in security frameworks, regulatory compliance (GDPR), data lineage, and quality managementAI/ML integration capabilities - Experience with MLOps, analytics platforms, and integrating AI/ML into data architecturesTechnical leadership at scale - Leading 15+ person cross-functional teams and serving as technical escalation point ...

control, review) that make ML/automation workflows intuitive and accessible. Collaborate with global stakeholders and architects to implement cost-effective data and MLOps pipelines; document methodologies. Build, package, and configure complex software systems across Linux. Improve efficiency, stability, and observability through profiling, metrics, and tracing. Validate work with methodical ...

including an understanding of performance, scaling and system architecture Strong programming skills in Python, with familiarity with ML frameworks and development workflows Experience with MLOps or production-oriented workflows, including environment management, reproducibility and deployment considerations Nice to have Experience training or optimizing ML systems Experience solving optimisation/MILP ...

machine learning, or a related field Authored publications in top‐tier journals and conferences (e.g. NeurIPS, ICLR, ICML, EMNLP) Experience working with MLOps infrastructure or deploying AI models to production, including the associated monitoring, testing and quality assurance Experience working with AI in regulated environments Created and optimised state ...

Event Hubs, or Kinesis for real-time data processing Knowledge of data architectures supporting AI/ML workloads, including vector databases, feature stores and MLOps pipelines Experience processing and managing unstructured data types (text, images, logs, sensor data) Understanding of Lambda and Kappa architectural patterns and when to apply each ...

Event Hubs, or Kinesis for real-time data processing Knowledge of data architectures supporting AI/ML workloads, including vector databases, feature stores and MLOps pipelines Experience processing and managing unstructured data types (text, images, logs, sensor data) Understanding of Lambda and Kappa architectural patterns and when to apply each ...

clients' AI ambitions and business outcomes into a hardware and platform roadmap, positioning NTT DATA's end-to-end portfolio — silicon, systems, storage, fabric, MLOps stack and managed services — to land service-led AI solutions. Lead integration of compute, storage, networking, the AI software stack (CUDA, ROCm, Triton, NIM, NVIDIA ...

Fuzzy Labs, a Manchester-based tech consultancy, seeks a Lead Applied AI Engineer to guide squads, own client projects from discovery to production, and shape technical direction. You'll mentor engineers, collaborate with customers, and ...

hackajob is partnering directly with Rentokil Initial to hire for this role. As part of our Group AI Team, we are scaling our internal capabilities to deliver cutting-edge, production-grade AI solutions across our ...

helping organisations solve complex operational, technical and regulatory challenges through AI adoption at scale.You will work across multidisciplinary teams, collaborating with data scientists, architects, MLOps/LLMOps engineers, business stakeholders and senior leadership to design, deliver and scale AI products, agentic AI solutions and data driven applications.Key responsibilities:·Building … Using Python and SQL to build robust, scalable AI and data solutions·Working with cloud platforms such as AWS, Azure, GCP or Databricks·Supporting MLOps, LLMOps, CI/CD and software engineering best practice·Collaborating with technical and non-technical stakeholders across complex programmes·Helping identify technical, delivery, security, data ...

maintain platform capabilities for model training, feature engineering, deployment and observability across cloud and on-prem environments. Build CI/CD pipelines for ML (MLOps) including automated testing, packaging, model registry workflows and promotion across environments. Implement containerised deployments (Docker/Kubernetes) and orchestration for batch and real-time inference … Strong software engineering skills in Python and/or Java/Scala, with clean coding, testing and code review practices. Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, Databricks, or equivalent. Proven experience with Kubernetes, Docker, Helm and infrastructure as code (Terraform/CloudFormation). ...

delivery of AI projects end to end from the silicon and data center design that underpins AI workloads, through the software and MLOps stack, to the governance frameworks that make AI trustworthy and defensible at scale. This is a technical hardware-and-software architect role, not a strategy-only position. … interconnects ,parallel/high-throughput storage, and orchestrationDesign the AI software stack: training and fine-tuning pipelines, distributed training frameworks, inference/serving platforms, MLOps/LLMOps tooling, vector databases, and retrieval-augmented generation (RAG) and agentic architectures. Define and AI governance frameworks covering model risk management, responsible AI, data ...