1 to 25 of 335 MLOps Jobs in the UK

maintainable code for production-grade AI solutions. Hands-on experience deploying AI/ML models to production environments, with a good understanding of MLOps practices including CI/CD pipelines, model versioning, and monitoring. Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such as SageMaker ...

data quality. Stay current with emerging AI and machine learning technologies and identify opportunities for innovation. Your Profile Essential Skills & Experience Proven experience in MLOps, data engineering, software engineering, or machine learning development with recent exposure to Generative AI and advanced analytics. Strong understanding of software engineering best practices, including ...

Desirable Experience Experience with any of the following would be highly beneficial: Large Language Models (LLMs) Transformers PyTorch Databricks Kubernetes Terraform Kafka Knowledge Graphs MLOps and model deployment best practices Technology Stack Python FastAPI SQL/PostgreSQL PyTorch Transformers/LLMs Databricks Terraform Kubernetes AWS & Azure Kafka Git CI/ ...

Azure OpenAI/AI Foundry, Google Vertex AI, Hugging Face Vector databases & retrieval: Pinecone, Weaviate, Chroma, pgvector, FAISS; embeddings, semantic and hybrid search, reranking MLOps/LLMOps & deployment: Docker, Kubernetes, FastAPI, CI/CD; observability, tracing, evaluation tooling (LangSmith, LangFuse); guardrails, prompt/version management Responsible AI & safety: bias & fairness ...

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). ...

platform capability and implementation strategy Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms ...

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 ...

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 ...

Implement scalable, resilient architectures suitable for production use in a regulated financial services environment.* Integrate AI services into existing business applications and workflows.**Data, MLOps & Engineering*** Work with Data Engineers and Analysts to prepare, embed, and manage data for AI use cases, including vector databases and embeddings.* Implement CI/ ...

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 ...

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 ...

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. ...

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. ...

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. ...

solutions on GCP leveraging Vertex AI, BigQuery, Cloud Run, secure API integrations; build scalable, resilient, compliant architectures; integrate AI services into existing applications. Data, MLOps & Engineering: Work with Data Engineers and Analysts to prepare, embed, and manage data (vector databases, embeddings); implement CI/CD, monitoring, logging, model performance tracking ...

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/ ...

French and English; advanced quantitative degree (MS/PhD) a plus. Skills Technology Vision System Architecture Cloud Migration AI Strategy ML/AI Productionization MLOps Microservices Event-driven Architecture DevOps/SRE CI/CD Infrastructure as Code Observability Incident Management Data Architecture Streaming Systems Client-facing Communication Stakeholder Management ...

documenting and defending architectural decisions and engaging in thoughtful technical debate. Not required, but it’s a plus if you also have: Experience with MLOps/LLMOps frameworks and best practices A PhD in Computer Science, Electrical Engineering or a related field. A background or work experience in life‐sciences ...

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/ ...

We're partnering with an organisation delivering a cutting-edge AI platform project. This is an opportunity to join a highly technical team building the cloud infrastructure and platform capabilities that power large-scale AI ...

business teams to deliver end-to-end solutions. Tech environment Python, LLMs (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, MLOps/LLMOps, AWS, Azure, GCP, Databricks, Kubernetes and GPU infrastructure. What we're looking for Proven enterprise AI or ML architecture design, strong cloud ...

such as Pinecone, Chroma or FAISS AI Agents and Generative AI applications Cloud platforms including AWS and/or Azure Modern ML pipelines and MLOps practices Agile delivery methodologies Experience working within Banking, Financial Services or a technology consultancy would be highly advantageous. Why apply? This is an opportunity ...

knowledge of database structure and efficiency Proficiency in Git Proficiency in AI/ML frameworks: Huggingface, Torch, TensorFlow, Scikit‐Learn, and Spacy Experience in MLOps platforms: MLFlow, dbt, Docker, Kubernetes Experience with cloud environments such as AWS Technical Concept Requirements Experience in NLP, Forecasting, Clustering, Recommendation Engines, Network Analysis (Graph ...

Generative AI. Demonstrated ability to architect end‐to‐end Data & AI solutions, with specific expertise in modern Generative AI concepts (e.g., fine‐tuning, RAG, MLOps for LLMs), using either open‐source and/or ISV tools such as Dataiku, Domino, DataRobot etc. Strong proficiency in a core programming language (e.g. ...

similar data processing technologies. Experience deploying and operating AI systems in cloud environments using containerisation, managed ML services or serverless architectures. Understanding of MLOps practices including model versioning, experiment tracking, CI/CD for models and monitoring of model performance and drift. Experience applying responsible AI principles, including data privacy ...