1 to 25 of 706 MLOps Jobs in the UK

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 ...

threat-intelligence vocabularies. GenAI and GraphRAG: LLM-based extraction, retrieval orchestration, agent/tool integration, prompt design, evaluation, grounding, guardrails, explainability, and evidence traceability. MLOps and observability: Experiment tracking, model versioning, deployment, monitoring, drift and quality checks, auditability, access controls, secrets management, and cost/performance management. Security Knowledge Graph ...

/ML/LLM/GenAI solutions to meet business objectives. Manage, mentor, and guide a team of ML and MLOps engineers. Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency. Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high ...

pipelines. Ability to analyse large datasets and communicate insights effectively. Experience with LLMs, generative AI, and prompt engineering. Knowledge of MLOps tools and frameworks (MLflow, Kubeflow, Airflow). Familiarity with SQL, NoSQL, and distributed data technologies. Experience deploying AI models in production environments. Publications, patents, or open-source contributions ...

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 ...

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). ...

/ML systems in production environments supporting large user bases Understanding of IT platforms i.e., cloud platforms (AWS, Azure, GCP), containerization (Kubernetes, Docker), and MLOps frameworks and practices Understanding of the use of Waterfall vs Agile practices including use of Kanban, SAFE, etc Deep understanding of telecommunications systems and integration ...

related field, or equivalent practical experience Nice-to-Have Skills AWS Certified Machine Learning Engineer – Associate or an equivalent Azure certification Experience with MLOps, model evaluation and observability tooling Experience with Terraform and Kubernetes #J-18808-Ljbffr ...

Experience with OpenAI, Azure OpenAI, Anthropic, LangChain, LlamaIndex, Hugging Face, or similar tools. Experience deploying AI models or AI applications into production. Knowledge of MLOps, CI/CD, monitoring, and model evaluation. Experience with containerisation tools such as Docker or Kubernetes. Understanding of AI governance, responsible AI, data privacy ...

pipelines. Ability to analyse large datasets and communicate insights effectively. Experience with LLMs, generative AI, and prompt engineering. Knowledge of MLOps tools and frameworks (MLflow, Kubeflow, Airflow). Familiarity with SQL, NoSQL, and distributed data technologies. Experience deploying AI models in production environments. Publications, patents, or open-source contributions ...

similar technologies. Knowledge of vector databases, embeddings, prompt engineering, and AI application architecture. Experience with Python for AI/ML-related development. Exposure to MLOps practices and AI model deployment pipelines. Contributions to open-source projects or technical communities. Competitive Salary Bonus Scheme Private Healthcare Insurance 25 Days Annual Leave ...

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. ...

Experience of working with client sponsors, both technical and non-technical, to collaboratively design requirements and build out solutions.* Experience of designing and implementing MLOPs strategy and framework and proven track record in designing and delivering AI/ML solutions at scale, from concept to production.* Deep understanding of Generative ...

REST APIs, microservices architecture Vector databases & embeddings Cloud platforms — Azure GCP, or AWS; deploying and managing AI/ML workloads at scale Version control & MLOps — Git, CI/CD pipelines, model versioning, monitoring in production Data handling Required — Experience & Mindset Demonstrated end-to-end delivery: problem → build → deployed solution → iteration ...

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 ...

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 ...

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 ...

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 ...

engineering tools (Pandas, Spark, dbt, LangChain, etc.) Exposure to modern software development practices, including version control (Git) and foundational concepts in DevOps and MLOps/LLMOps and CI/CD principles Exceptional time management in a complex and largely autonomous work environment Willingness to learn quickly and adapt to different ...

Snowflake (SQL, Snowpark, performance tuning) Strong SQL and/or Python capability, and familiarity with tools such as dbt, Git, CI/CD, or MLOps frameworks. Understanding of BI/visualisation principles (Power BI/Tableau) and downstream analytics requirements. Our commitment:Wavestone values and Positive Way Elevateclient satisfactionby impacting ...

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 ...

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 ...

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 ...

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 ...

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 ...