51 to 75 of 819 MLOps Jobs in the UK

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. ...

optimize relational data models using PostgreSQL. Deploy and manage backend services on cloud platforms, preferably Azure and/or GCP. Collaborate with DevOps/MLOps engineers on CI/CD pipelines, containerization, and deployment strategies. Monitor service health and performance, proactively addressing bottlenecks and reliability issues. Work within an Agile ...

experience and a track record of building deployment and release automationExperience serving, scaling, and monitoring ML models or data-intensive services in production (MLOps)Practical experience with observability tooling (metrics, logging, tracing) and production incident responseExperience operating ML/LLM workloads in production (LLMOps, inference reliability, cost/performance management ...

experience and a track record of building deployment and release automation Experience serving, scaling, and monitoring ML models or data-intensive services in production (MLOps) Practical experience with observability tooling (metrics, logging, tracing) and production incident response Experience operating ML/LLM workloads in production (LLMOps, inference reliability, cost/ ...

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 stack Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds Preferred qualifications, capabilities, and skillsExperience with data ...

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 ...

access management with OIDC/SAML and Azure Entra ID.* Experience with LLMs, RAG systems, prompt engineering and AI evaluation frameworks; familiarity with MLOps, model deployment and AI observability/guardrails; working use of AI-assisted development tools (e.g. Claude Code).* Familiarity with one or more of: trading ...

Azure, Databricks, and Snowflake.Extend and support a multi-cloud and hybrid cloud platform for Analytics workloads.Drive agile engineering practices, including CI/CD, DevSecOps, MLOps/LLMOps, automated testing, and engineering best practices.Collaborate with multi-functional teams to integrate AI agents, automation capabilities, and modern AI services into business workflows.Ensure ...

Flows).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.Education - BS in Computer Science, Engineering, Data Science, or equivalent technical fieldUnleash Your PotentialWhen ...

communication skills and experience working across engineering, data science, security, architecture, and product teams.Bonus pointsExperience 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 platforms ...

environments. Experience defining scalable AI architectures on cloud platforms such as Azure, AWS or GCP, leveraging cloud-native AI and data services. Understanding of MLOps and GenAIOps principles, including lifecycle management, monitoring, performance optimisation and operationalisation at scale. Exceptional stakeholder management, communication and leadership skills, with the ability to translate ...

Hands-on experience in developing and deploying AI solutions in a professional setting.Experience building data pipelines and working with structured and unstructured data.Experience with MLOps tools and best practices for model deployment, monitoring, and governance.Experience working in Agile delivery environments.Strong analytical and problem-solving skills, with attention to detail.Programming: Strong ...

e.g., AI coding assistants, automated testing generation) to enhance developer velocity and code quality across the portfolio.* Data Readiness: Architect robust, scalable data pipelines (MLOps/LLMOps) to support reliable model training, fine-tuning, and low-latency inference.**Technical Leadership & Governance*** Act as a senior technical leader and mentor ...

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 ...

backend development — FastAPI, REST APIs, microservices architectureVector databases & embeddingsCloud platforms — Azure GCP, or AWS; deploying and managing AI/ML workloads at scaleVersion control & MLOps — Git, CI/CD pipelines, model versioning, monitoring in productionData handlingRequired — Experience & MindsetDemonstrated end-to-end delivery: problem build deployed solution iterationStrong experience with Generative ...

Advise clients on AI platform capability and implementation strategyTech environment: Python LLMs (prompt engineering, fine-tuning, RAG) LangChain/LangGraph Agent frameworks Vector databases MLOps/LLMOps AWS/Azure/GCP/Databricks Kubernetes & GPU infrastructureWhat you'll bring:- Experience designing enterprise AI or ML architectures- Strong grasp ...

clients on AI platform capability and implementation strategy Tech environment: Python LLMs (prompt engineering, fine-tuning, RAG) LangChain/LangGraph Agent frameworks Vector databases MLOps/LLMOps AWS/Azure/GCP/Databricks Kubernetes & GPU infrastructure What you'll bring: - Experience designing enterprise AI or ML architectures - Strong grasp ...