51 to 75 of 702 MLOps Jobs in the UK

Computer Vision models, Speech models, Vision‐Language models, and other modalities. Experience with AI model optimization, quantization, or deployment to edge devices. Familiarity with MLOps tools and practices: CI/CD, monitoring, and orchestration frameworks (e.g., Kubeflow, Flyte, MLflow); proficiency with Docker and Kubernetes for AI workload containerization. Hands ...

PyTorch, or Java-based ML libraries) Strong problem-solving and communication skills Preferred Qualifications Experience with NLP, computer vision, or recommendation systems Familiarity with MLOps practices and model lifecycle management Knowledge of event-driven architectures (Kafka or similar) Experience with DevOps tooling and infrastructure as code Exposure to Generative ...

across financial services, healthcare, manufacturing, retail, public sector, or other enterprise industries. Cloud & Infrastructure: Hands‐on experience with cloud architecture, Infrastructure‐as‐Code, DevOps, MLOps, or enterprise platform engineering. Pre‐sales & Customer Success: Experience supporting enterprise sales cycles, solution demonstrations, technical workshops, or proof‐of‐concept engagements. Enterprise Integrations: Experience ...

Data Mesh or Lakehouse architectures. Familiarity with Kubernetes, Docker, and real-time streaming technologies (e.g. Kafka, Kinesis). Exposure to ML engineering pipelines or MLOps frameworks. The Zego ways of working Teams work better with time to collaborate and space to get things done. We call it Zego Hybrid: some ...

responsible AI principlesFamiliarity with cloud platforms including Azure AWS or GCP.Desirable (Not Mandatory):Experience implementing AI testing frameworks and governance controlsKnowledge of MLOps and model lifecycle managementUnderstanding of AI risk management and regulatory compliance frameworksCertifications in AI Cloud Quality Engineering or Test AutomationWe are a Disability Confident Employer:Capgemini ...

graph databases (e.g. Postgres, Neo4j) to model and serve data behind AI applications Work closely with platform engineering throughout deployment - hosting, scaling, and MLOps/LLMOps - without owning that infrastructure yourself Define AI governance and responsible-use guardrails: data privacy boundaries, LLM governance, and policy enforcement (OPA/Rego) Instrument ...

data science solutions. Ensure sharing best practices and learnings with other squad members. Design and spearhead the development of Gen AI, AI Agents and MLOps platforms, applications, and solutions driven by GenAI, deep learning and machine learning to address business challenges and elevate product performance. Build standards and recommendations ...

native data services and event-driven architectures Experience building data products that support analytics, reporting, machine learning, or AI use cases Knowledge of MLOps, AI Engineering, Agentic AI, or Generative AI implementation patterns Experience leading distributed engineering teams Experience establishing engineering standards, reusable frameworks, and community-of-practice initiatives Knowledge ...

native data services and event-driven architectures Experience building data products that support analytics, reporting, machine learning, or AI use cases Knowledge of MLOps, AI Engineering, Agentic AI, or Generative AI implementation patterns Experience leading distributed engineering teams Experience establishing engineering standards, reusable frameworks, and community-of-practice initiatives Knowledge ...

workflow optimisation. Cloud and Deployment Knowledge – Familiarity with deploying AI models and AI‐enabled services using cloud platforms such as AWS, Azure, or GCP, MLOps practices, cloud architecture, containerisation, model monitoring, and secure production deployment patterns. Problem‐Solving and Communication skills – Ability to translate complex business and engineering challenges into ...

native data services and event-driven architectures Experience building data products that support analytics, reporting, machine learning, or AI use cases Knowledge of MLOps, AI Engineering, Agentic AI, or Generative AI implementation patterns Experience leading distributed engineering teams Experience establishing engineering standards, reusable frameworks, and community-of-practice initiatives Knowledge ...

Skills: Hands-on experience with data lineage tools and techniques, including graph & vector databases and metadata management platforms Hands-on experience with LLM Ops, MLOps, and AI/ML platform deployment at scale Familiarity with business-led analytics delivery models and rapid prototyping frameworks Experience with AI/ML governance ...

Prefect.* **Data Quality and Lineage** Familiarity with tools and techniques for data testing, documentation and lineage. Examples include Great Expectations or dbt.* **AI and MLOps** Understanding of how to operationalise machine learning models in production, including model packaging, monitoring and controlled deployment.* **Containerisation and Kubernetes** Confidence deploying applications in containerised ...

security capabilities, ensuring health, telemetry completeness, integration fidelity, detection coverage and continuous service improvement; partner across SOC, cloud, identity, data, application security, DevSecOps, MLOps, platform engineering and architecture; deliver executive-ready dashboards and reporting. Governance, Risk and Regulated-Environment Assurance: Translate policy, regulatory and framework expectations into testable requirements ...

Experience with Generative AI, LLMs, RAG architectures or agent‐based AI solutions. SQL and large‐scale data analysis. Cloud‐based data science platforms and MLOps practices. Data visualisation and storytelling tools. Desirable Skills Experience within financial services, regulated environments or market infrastructure. Familiarity with Azure AI services and Microsoft ...

personal to all of us.” – Julie Sweet, Accenture CEO. About Accenture We are a dynamic team of specialists in Data Science, Data Engineering, and MLOps, dedicated to delivering cutting‐edge AI solutions within secure government and defence environments. Join us to be part of an inclusive and collaborative environment where ...

Relevant cloud, AI, or security certifications. Experience deploying ML models into production environments. Experience with Kubernetes, OpenShift, or cloud-native deployment architectures. Experience with MLOps tooling (e.g. MLflow, Kubeflow, model monitoring platforms). Experience applying DevSecOps practices within cloud or controlled environments. Exposure to large language models or generative ...

tool use/function calling, RAG, agents, prompt engineering, or agentic patterns for enterprise workflows. Production ML/LLMOps: Experience with model deployment, monitoring, MLOps/LLMOps, and lifecycle management across cloud services (AWS Bedrock, Azure AI, GCP Vertex AI). AI Architecture & Security: Understanding of cloud reference architectures, identity ...

Microsoft Purview or comparable tools. Experience with search indexes, vector databases, graph databases, knowledge graphs, ontologies or master-data-management techniques. Experience with MLOps, model monitoring, experimental design, causal inference, forecasting, recommendation systems or optimisation. Experience with legal, financial, client, market or professional-services data, especially licensed external data ...

Nice to Have Experience with multimodal reasoning Experience with edge or offline AI deployments Familiarity with Kubernetes (EKS/OpenShift) for managing deployed applications MLOps experience: model evaluation, monitoring, reproducibility Observability tooling for agentic systems (model drift, agent behaviour, performance monitoring) Experience with agent orchestration patterns and inter‐agent communication ...

Nice to Have Experience with multimodal reasoning Experience with edge or offline AI deployments Familiarity with Kubernetes (EKS/OpenShift) for managing deployed applications MLOps experience: model evaluation, monitoring, reproducibility Observability tooling for agentic systems (model drift, agent behaviour, performance monitoring) Experience with agent orchestration patterns and inter‐agent communication ...

Government or Public Sector environments (essential) Eligible to obtain and maintain BPSS clearance Desirable Experience Experience delivering AI-enabled mobile applications Knowledge of MLOps, DevSecOps, CI/CD, and Infrastructure as Code Experience with vector databases, Retrieval-Augmented Generation (RAG), and AI agent frameworks Exposure to GDS standards and government ...

Professional Services, or Advisory environments. Knowledge of FinOps, cloud cost optimisation, and total cost of ownership considerations. Relevant cloud and data certifications. Exposure to MLOps and AI platform engineering. Why Join? Define the organisation's data and AI strategy from the ground up. Work directly with senior leadership and influence ...

high‐impact team managing enterprise‐scale Azure solutions. Work on the intersection of data, AI, DevOps, and automation. Opportunities to grow across data engineering, MLOps, and cloud automation. A dynamic, learning‐focused work environment with cutting‐edge tools and processes. #J-18808-Ljbffr ...

London office once per week. Desirable Experience AI orchestration and workflow frameworks. Knowledge graph or semantic search technologies. Model fine-tuning and evaluation. MLOps, CI/CD, containerisation, and cloud-native deployment practices. Experience developing autonomous agents and reasoning systems. Why Join? Join a high-calibre team building a genuinely ...