1 to 25 of 137 Remote/Hybrid MLOps Jobs in London

following would be highly advantageous: Large Language Models (LLMs) Amazon Bedrock Azure AI Foundry PyTorch FastAPI Knowledge Graphs Kubernetes Docker Infrastructure as Code MLOps Event-Driven Architecture Vector Databases Retrieval-Augmented Generation (RAG) Technology Stack Python SQL FastAPI PyTorch Docker Kubernetes AWS Azure Git CI/CD Knowledge Graphs ...

Machine Learning Engineer, ML Engineer, Full Stack Engineer, Full Stack Developer, Python Developer, Senior Consultant Data Science, Data Scientist, Solutions Architect AI, LLM Engineer, MLOps Engineer, AI Software Engineer, Django Developer, FastAPI Developer, React Developer, Next.js Developer, TypeScript Developer, Azure OpenAI, Vertex AI, LangChain, LangGraph, CrewAI, Autogen, OpenAI Agents ...

management, agent orchestration, guardrails, and responsible AI practices.* Experience integrating AI solutions with enterprise systems, data platforms, APIs, and cloud-native services.* Knowledge of MLOps, CI/CD, monitoring, model lifecycle management, and production support processes.* Familiarity with cloud security, governance, risk management, and compliance requirements for enterprise AI solutions. ...

LangGraph, CrewAI, AutoGen); mentoring specialists without formal authority; AI governance (security, privacy, observability) Desirable: cloud platforms (AWS/Azure/GCP); Docker/Kubernetes; MLOps/DataOps monitoring and testing Personal Attributes: transversal mindset, proactive problem-solver or systems thinker, continuous learner, adaptable and pragmatic Interested? Whether your strength ...

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 ...

effectively within agile, cross-functional teams. Desirableskills andexperience: Experience building platforms or applications that support Data Science, Machine Learning, or AI workloads. Experience with MLOps tooling such as MLflow, Kubeflow, Vertex AI, or similar technologies. Knowledge of workflow orchestration platforms such as Apache Airflow. Experience with data processing technologies such ...

data with efficiency and ease. You will work closely with people from a wide variety of domains: Product Data Scientists, Analytics Engineers, ML Engineers, MLOps, MarTech and other Data Engineering teams. You will help manage our growing data needs and support increasingly complex business problems by building and promoting self ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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. ...

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 ...

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 ...

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. Pay Transparency At Zego, we are committed to pay transparency and equal pay practices. In compliance with EU pay transparency regulations, we proactively disclose ...

experience with RAG architectures, Agentic AI frameworks, and orchestration tools such as LangChain, LangGraph, AutoGen, and CrewAI. Proven experience applying Agile delivery, DevOps, MLOps, and LLMOps practices to enterprise AI programmes. Strong cloud architecture experience across Azure, AWS, or GCP. Experience in model deployment, monitoring, observability, governance, versioning, and operational ...