26 to 50 of 697 MLOps Jobs in England

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 ...

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 ...

TensorFlow, PyTorch, or Java-based ML libraries) Strong problem-solving and communication skillsPreferred 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 ...

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 ...

degree or equivalent work experience.Preferred Skills:Experience with OpenAI, Anthropic, Mistral, Google/Gemini, Azure, AWS, GCP, Databricks, Snowflake, vector databases, model registries, and MLOps/LLMOps tooling.Experience working directly with clients, sales teams, or product teams to rapidly prototype solutions under ambiguous conditions.AI/ML, cloud, data engineering ...

equivalent cloud‐native AI/ML platforms (e.g. AWS SageMaker, AzureML) and agent orchestration frameworks (e.g. LangChain, LangGraph, ADK). Solid understanding of MLOps – CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying and operating ML systems in production (batch and real‐time). Familiarity ...

system design. Experience deploying and operating AI and machine learning services in production, including API-based architectures and cloud-hosted workloads. Practical experience with MLOps and AI lifecycle management, including data, model and environment versioning, automated deployment pipelines, monitoring and model retraining workflows. Experience leading complex technical projects from initial ...

latest model capabilities and AI product stacks.Machine Learning Engineering: End-to-end ML lifecycle expertise: feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). ...

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/ ...

operational controls).Broad experience across major cloud platforms (AWS, Azure, GCP) and their AI/GenAI services; cloud-agnostic experience is preferred.Good grasp of MLOps/LLMOps practices (e.g., CI/CD, model/LLM monitoring, controls and auditability) and how to embed them pragmatically within client delivery.Familiarity with large ...

Generative AI systems.Broad experience across major cloud platforms (AWS, Azure, GCP) with Generative AI services. Cloud-agnostic experience is preferred.Strong grasp of MLOps/LLMOps principles, including CI/CD for ML, model monitoring, and governance frameworks.Proficiency with large-scale data processing and storage technologies (SQL, Spark, Hadoop ...

latest model capabilities and AI product stacks.Machine Learning Engineering: End-to-end ML lifecycle expertise: feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). ...

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 ...

Intelligence, Data Science, or a related technology discipline Minimum 2:1 degree classification (or equivalent) Desirable Azure OpenAI, AWS Bedrock and Google Gemini experience MLOps, Data Engineering or Platform Engineering experience Experience leading technical teams Knowledge of Agentic AI, AI Agents and emerging AI frameworks What's On Offer? Opportunity ...

Intelligence, Data Science, or a related technology discipline Minimum 2:1 degree classification (or equivalent) Desirable Azure OpenAI, AWS Bedrock and Google Gemini experience MLOps, Data Engineering or Platform Engineering experience Experience leading technical teams Knowledge of Agentic AI, AI Agents and emerging AI frameworks What's On Offer? Opportunity ...

DevOps, and cloud-native architecturesPractical experience with Azure (Azure OpenAI, Azure AI Foundry, AI Search) and familiarity with AWS or GCPProficiency with containerized deployment, MLOps tooling (MLFlow, Airflow), and infrastructure-as-code practicesBachelor’s or Master’s degree in Computer Science, Data Science, or related fieldExperience with Azure AI Agent ...

orchestration frameworks, shipped to real users.• Azure & Databricks - not expert level, similar can work but someone who has clear experience with cloud infrastructure• MLOps - CI/CD, containerisation, monitoring, evaluation frameworks.• Data at scale - large, complex datasets and robust pipelines for training, fine-tuning and inference.Not for: solution architects, software ...

patterns Background in NLP or contributions to open‐source AI/ML projects Experience with model fine‐tuning or custom training approaches Familiarity with MLOps platforms and experiment tracking tools Experience with infrastructure as code (Terraform, CloudFormation) Location: Third Floor, 1 Dean Street, London W1D 3RB, United Kingdom. Full time. ...

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

experience developing Production-scale Cloud-native data engineering solutions in commercial environments Familiarity with Cloud Data engineering services (e.g., ETL, Glue, S3, Athena) and MLOps stackDemonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting ...

skills and experience Experience with Microsoft Azure platform services Azure OpenAI, Azure AI Foundry or related AI platforms Platform Engineering and Developer Experience practices MLOps, model deployment and model lifecycle management Experience operating platforms in large-scale enterprise environments Knowledge of generative AI architectures, AI gateways, agent frameworks or agentic ...

explainable and reliable outcomes.Integrate AI agents and automation solutions with core applications, APIs, digital channels and enterprise data services.Productionise solutions using appropriate DevOps and MLOps practices, with a focus on reliability, scalability, observability and ongoing support.Monitor agent performance, drift, hallucination risk, unintended actions, incidents and service degradation.Implement appropriate controls, guardrails ...

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 ...

/ML workloads and automate provisioning across cloud environments using Infrastructure as Code (IaC).MLOps & LLMOps Engineering: Architect, maintain, and optimise robust MLOps/LLMOps pipelines and CI/CD frameworks for continuous model deployment.GenAI Production Deployment: Deploy Large Language Models (LLMs) into production environments, ensuring high availability, low latency ...

Experience building 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.BenefitsWhat it’s like to work at ZegoAt Zego, we operate like a high-performing sports team. We come together as one, driven by ambition ...