51 to 75 of 603 MLOps Jobs in England

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 ...

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 ...

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 ...

technical and business stakeholders. Desirable Experience with Unity Catalog or similar data governance frameworks. Experience with Terraform or Databricks Asset Bundles. Exposure to MLflow, MLOps or AI and Generative AI workloads. Experience with streaming technologies such as Kafka or Structured Streaming. Databricks certification. Previous consulting or client-facing delivery experience. ...

Azure and Databricks, and familiarity with Azure DevOps tools like Boards, Repos, and Pipelines. Good knowledge of explainable AI and best practices in MLOps and LLMOps, with the ability to explain their importance. Strong programming skills in Python and SQL, with the ability to present data insights clearly to different ...

Azure and Databricks, and familiarity with Azure DevOps tools like Boards, Repos, and Pipelines. Good knowledge of explainable AI and best practices in MLOps and LLMOps, with the ability to explain their importance. Strong programming skills in Python and SQL, with the ability to present data insights clearly to different ...

Azure and Databricks, and familiarity with Azure DevOps tools like Boards, Repos, and Pipelines. Good knowledge of explainable AI and best practices in MLOps and LLMOps, with the ability to explain their importance. Strong programming skills in Python and SQL, with the ability to present data insights clearly to different ...

inference. 4 Implement robust LLM evaluation frameworks to monitor output quality (hallucinations, relevance), manage token costs and ensure content safety/governance. 5 Utilise MLOps tools and Google’s Agent Development Kit (ADK) to design autonomous agents and optimise deployment workflows on Vertex AI. 6 Lead ...

Azure and Databricks, and familiarity with Azure DevOps tools like Boards, Repos, and Pipelines. Good knowledge of explainable AI and best practices in MLOps and LLMOps, with the ability to explain their importance. Strong programming skills in Python and SQL, with the ability to present data insights clearly to different ...

threat-intelligence vocabularies. GenAI and GraphRAG : LLM-based extraction, retrieval orchestration, agent/tool integration, prompt design, evaluation, grounding, guardrails, explainability, and evidence traceability. MLOps and observability: Experiment tracking, model versioning, deployment, monitoring, drift and quality checks, auditability, access controls, secrets management, and cost/performance management. Security Knowledge Graph ...

infrastructure Transform innovative data science prototypes into robust, scalable, and secure production software, taking ownership of the 'path to production' Drive the adoption of MLOps best practices (CI/CD for ML, model versioning, feature stores) to accelerate the feedback loop for Data Scientists Effectively communicate the complexities ...

Multimodal Pipelines - Prior exposure to conversational voice pipelines or multimodal integrations via hyperscaler services. Advanced AI/ML Frameworks - Exposure to frameworks (TensorFlow, PyTorch), MLOps practices, and cloud AI platforms like Google Vertex AI or AWS Sagemaker. #J-18808-Ljbffr ...