26 to 50 of 537 MLOps Jobs in London

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). ...

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 ...

/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 ...

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 ...

development (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, FastAPI, and AWS, Azure, GCP or Databricks. CI/CD, MLOps and LLMOps familiarity expected. Strong communication and stakeholder skills matter at every level. ...

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 ...

high-impact team managing enterprise-scale Azure solutionsWork on the intersection of data, AI, DevOps, and automationOpportunities to grow across data engineering, MLOps, and cloud automationA dynamic, learning-focused work environment with cutting-edge tools and processes ...

technical audiences and apply AI-enabled approaches to deliver practical business value.Bonus Points ForParticipation in Kaggle or other data science competitions.Experience with MLOps practices (CI/CD, model monitoring, DevOps integration).Familiarity with advanced NLP frameworks such as spaCy or Transformers and exposure to AI technologies including Generative ...

cybersecurity, or data platform certifications.Experience with platforms such as Azure, AWS, GCP, OpenAI, Anthropic, Mistral, Google/Gemini, Databricks, Snowflake, Kubernetes, vector databases, and MLOps/LLMOps tooling.Strong executive presence, technical storytelling, mentorship, stakeholder influence, and ability to simplify complex concepts for business and technical audiences.Ability to travel as required ...