26 to 50 of 603 MLOps Jobs in England

systems, or complex AI orchestration platforms that go beyond simple LLM integrations Advanced cloud-native ML infrastructure – Deep expertise building sophisticated ML infrastructure, MLOps pipelines, model serving platforms, and cloud-native AI systems at scale; experience optimizing cost and performance of production LLM deployments Financial services or analytical domain knowledge ...

e.g. LangChain/LangGraph) Python and modern data engineering Snowflake and governed semantic layers AWS/GCP cloud services Vector databases Data pipelines and MLOps tooling Business intelligence and visualisation tools What’s in it For You? Hybrid Work Model: We’ve adopted a flexible hybrid working environment ...

We're partnering with an organisation delivering a cutting-edge AI platform project. This is an opportunity to join a highly technical team building the cloud infrastructure and platform capabilities that power large-scale AI ...

Computer Science, Machine Learning, or related field Nice to Have Published research in top ML venues (NeurIPS, ICML, ACL, CVPR). Experience with MLOps tools: MLflow, Weights & Biases, DVC. Familiarity with distributed training frameworks (DeepSpeed, Horovod). Experience with LLM fine-tuning and prompt engineering. Contributions to open-source ...

measurable outcomes. Nice to Have Experience with Bayesian modelling, optimisation algorithms or multi-armed bandits. Exposure to Generative AI, LLMs or agentic AI systems. MLOps experience, including CI/CD, model monitoring and deployment pipelines. Experience working within insurance, fintech, travel, ecommerce or other highly transactional environments. What ...

databases (Microsoft SQL Server or equivalent) and NoSQL conceptsFamiliarity with microservices, API-first architecture and DevOps principlesNice to haveKnowledge of Azure AI Foundry, MLOps workflows and observability for AI modelsHands-on experience with Databricks for data processing in analytics-driven solutionsUnderstanding of RAG architectures, vector databases and advanced conversational ...

Factory and DatabricksOperationalize ML solutions, integrating feature stores, model registries and inference endpointsCollaborate with data scientists to deploy and monitor AI models using enterprise MLOps frameworksIntegrate GenAI-assisted development methods into data workflows for automation and efficiencyEnsure data governance, lineage, cataloging and quality frameworks across platformsOptimize platform performance, manage compute ...

optimization and ML theory. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.) Specialism or well-researched interest in NLP Broad knowledge of MLOps tooling – for versioning, reproducibility, observability etc. Experience monitoring, maintaining, enhancing existing models over an extended time period Extensive experience with pytorch and related data science ...

work effectively in a collaborative team environment with strong problem‐solving and execution skills Experience working with Agile development methodologies Familiarity with DevOps and MLOps practices, including CI/CD pipelines, infrastructure automation, and deployment workflows Exposure to vector databases, embeddings, or orchestration frameworks used in modern AI applications Strong ...

across OCI, Data Management, AI Services or Autonomous Database. Experience working with complementary technologies such as Python, Spark, Databricks, Azure, AWS, OpenShift, Kubernetes or MLOps platforms. Demonstrated ability to structure client problems, shape solutions and identify opportunities for further value creation. Ability to travel as required to support client engagements. ...

evaluation engineering, and LLM guardrails/safety. Strong foundations in machine learning and deep learning, including distributed training, GPU workloads, and the full MLOps lifecycle (tracking, registry, serving, monitoring). Proficiency in Python and the ML ecosystem (PyTorch/Transformers, MLflow, Spark), and comfort building production-quality reference implementations. Experience ...

frameworks (eg LangChain/LlamaIndex-style tooling, multi-agent patterns) - Prompt engineering and systematic evaluation/guardrail tooling (hallucination detection, safety testing) - LLMOps/MLOps - model versioning, deployment, monitoring, and rollback for AI services in production - Cloud-native engineering across major providers (AWS, Azure, or GCP), including their AI/ ...

experience working across engineering, data science, security, architecture, and product teams. Bonus points Experience with AI or machine learning platforms, model‐serving infrastructure, MLOps, or data‐intensive services. Knowledge of advanced networking, API management, distributed systems, service mesh, or zero‐trust architecture. Experience with GitOps, policy as code, internal developer ...

Job Type: Permanent Build a brilliant future with Hiscox Company description Hiscox is a diversified international insurance group with a powerful brand, strong balance sheet and plenty of room to grow. Listed on the London ...

activities Partner with Data Engineers, Architects, Product Owners, and stakeholders throughout the delivery lifecycle Support the operationalisation of AI solutions through APIs, cloud platforms, MLOps, and downstream integration patterns Provide technical leadership and mentoring to junior practitioners Contribute to AI best practices, governance, responsible AI adoption, and continuous improvement initiatives … operationalising AI solutions in production environments Experience with vector databases, knowledge graphs, GraphRAG, entity resolution, recommendation systems, or semantic retrieval architectures Experience with MLOps, model monitoring, observability, and AI platform engineering Experience working within regulated or high-assurance environments, including Public Sector, Defence, Financial Services, or Critical National Infrastructure Experience ...

models into Azure enterprise environments. Azure Databricks, Azure Machine Learning and ideally Azure AI Foundry. Building production AI applications using LangChain, LangGraph and Python. MLOps/LLMOps including deployment, monitoring, governance and lifecycle management. RAG, vector databases, prompt engineering and tool‐calling patterns. Building end-to-end AI/ ...

libraries (e.g., spaCy, NumPy, SciPy, Transformers). Understanding of big data and cloud technologies (Spark, Hadoop, Azure, AWS, or GCP). Deep expertise in MLOps principles, scalable model deployment, and model governance. Excellent communication and stakeholder management skills with the ability to translate data insights into business value. Bonus Points ...

system interactions Culture and Innovation Contribute to a culture of engineering excellence, collaboration, and continuous learning Stay curious about emerging tools and approaches in MLOps and applied AI, and bring ideas for improvement to the team Support responsible AI practices, contributing to explainability, auditability, and fairness in ML systems What ...

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 ...