1 to 25 of 204 MLOps Jobs in London

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 ...

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 ...

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 ...

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 ...

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 ...

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. ...

models into Azure enterprise environments. Azure Databricks, Azure Machine Learning and ideally Azure AI Foundry. Building production AI applications using LangChain, LangGraph and Python. MLOps/LLMOps including deployment, monitoring, governance and lifecycle management. RAG, vector databases, prompt engineering and tool-calling patterns. Building end-to-end AI/ ...

required) Experience with AI frameworks such as LangChain, LangGraph or similar Experience deploying AI workloads within AWS, Azure or GCP Knowledge of MLOps, containerisation or Kubernetes Experience working on National Security, Defence or Intelligence programmes Familiarity with secure software development within highly classified environments ...

solution lifecycle—assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and MLOps/LLMOps pipelines for training and production, and ensuring quality, value, and reliability of deployed systems. THE WORK Formulate real-world problems into practical, efficient ...

expertise preferred, with hands‐on exposure to AWS and GCP. Experience integrating AI into distributed systems, microservices, APIs, and event‐driven architectures. DevOps and MLOps experience including CI/CD, IaC, automated testing, observability, and deployment automation. Familiarity with data engineering, ML models, Responsible AI, and enterprise governance. Ability ...

domains. Ability to lead architectural discussions and technical trade‐offs without losing sight of the details. Familiarity with cloud infrastructure (AWS preferred) and modern MLOps practices. Commitment to people development: mentoring, performance conversations, and building high‐performing teams. Bonus: experience with NLP on financial contracts, RAG or LLM‐based applications ...

Nice to have Experience in one or more of our domains (assessment/psychometrics, workforce skills/ontologies, recommendations, fraud detection). Familiarity with MLOps practices (CI/CD for ML, experiment tracking, data/version control) in a cloud environment. How we work at Pearson Purpose‐driven, learner‐first ...

scalable enterprise workloads, integrating with clients' incumbent technology estates. Implement hybrid-cloud and multi-cloud strategies using modern data infrastructure (Kafka, Spark, data lakes), MLOps practices, and deployment platforms. Ensure compliance with data privacy regulations (including UK GDPR) and responsible AI standards, incorporating governance, observability, and security controls into ...

science tooling, including: Version control (Git) Cloud platforms (AWS, GCP, or Azure) Experiment tracking and model management Workflow orchestration and containerisation Strong understanding of MLOps principles, including model monitoring, retraining, and lifecycle management. Proven ability to translate business problems into data science solutions that deliver measurable outcomes. Beneficial Exposure ...

expertise preferred, with hands‐on exposure to AWS and GCP. Experience integrating AI into distributed systems, microservices, APIs, and event‐driven architectures. DevOps and MLOps experience including CI/CD, IaC, automated testing, observability, and deployment automation. Familiarity with data engineering, ML models, Responsible AI, and enterprise governance. Ability ...

practices. Hands‐on experience with Distributed Training infrastructure (Multi‐node GPU training, Kubernetes, vLLM). Familiarity with Deep Learning frameworks (PyTorch). Experience with MLOps tools and experiment tracking (e.g., ClearML, MLFlow, Weights & Biases). Research Fluency: Ability to read technical research papers and translate them into engineering requirements. ...

build scalable pipelines, develop and evaluate models and help deliver cloud-based AI solutions across Azure, AWS or GCP. You'll also contribute to MLOps practices, support production deployments and work with both structured and unstructured datasets. You'll collaborate closely with engineers, data professionals and business stakeholders to turn ...

following attributes: Experience working with large-scale transactional, behavioural, or operational datasets Experience with cloud platforms such as Azure, AWS, or GCP Knowledge of MLOps, model monitoring, and deployment frameworks Exposure to Generative AI, Large Language Models (LLMs), or modern AI workflows Strong commercial awareness and the ability to translate ...