1 to 25 of 532 Permanent 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 ...

. Prompt Engineering, Langchain,Langsmith/Langfuse, Gen AI/Agentic AI , Cloud platforms (Azure AI Foundry, AWS Bedrock/Sagemaker, GCP Vertex AI) , MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes).Preferred•Delivered AI projects within Agile frameworks•Experience on Gen AI Feedback Analysis, topic modelling, sentiment analysis•Knowledge ...

/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 ...

model development and deployment. Working knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation and evaluation methods. Experience with MLOps and engineering best practices, including CI/CD pipelines, model versioning, monitoring and automated testing. Experience deploying scalable AI solutions on cloud platforms such ...

.Experience building and deploying Generative AI solutions, including prompt engineering, fine-tuning, and evaluation of large language models (LLMs) or other generative architectures.Understanding of MLOps principles, including automated model training, CI/CD for ML, model versioning, monitoring, and retraining.Experience deploying AI and GenAI solutions on cloud platforms (AWS, Azure ...

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 patterns ...

exposure to containerization technologies such as Docker and Kubernetes would be beneficialExposure to software engineering concepts and best practices, including DevOps, DataOps, and MLOps, will be advantageousWhile we advocate using the right tech for the right task, we often leverage: Python, PySpark, the PyData stack, SQL, Airflow, Databricks, Kedro ...

PyTorch, or Java-based ML libraries) Strong problem-solving and communication skills Preferred Qualifications Experience with NLP, computer vision, or recommendation systems Familiarity with MLOps practices and model lifecycle management Knowledge of event-driven architectures (Kafka or similar) Experience with DevOps tooling and infrastructure as code Exposure to Generative ...

compliant deployment of AI and Generative AI systems.Experience across one or more major cloud platforms (AWS, Azure, GCP) with Generative AI services.Grasp of MLOps/LLMOps principles, including CI/CD for ML, model monitoring, and governance frameworks.Proficiency with data processing and storage technologies (eg SQL, Spark)Strong stakeholder management ...

secure networking.Ability to align AI solutions with governance, compliance, and Responsible AI requirements.Proven experience leading delivery and coordinating multidisciplinary engineering teams.DesirablePractical experience implementing MLOps and LLMOps practices (CI/CD, automated testing, model lineage, reproducibility).Experience with infrastructure automation and platform engineering (e.g. Terraform, Docker, Kubernetes).Experience creating reusable engineering ...

LLMs, Retrieval-Augmented Generation (RAG), AI agents, prompt engineering, and model evaluation.Experience building cloud-native applications on Azure, AWS, or Google Cloud.Strong understanding of MLOps, CI/CD, Kubernetes, containerisation, and scalable system architecture.Experience integrating AI into customer-facing products.Excellent communication skills with the ability to influence technical ...

distributed computing frameworks (e.g., Spark, Dask), cloud platforms (e.g., AWS, Azure, GCP), and analytics libraries (e.g., pandas, numpy, matplotlib)Exposure to DevOps, DataOps, and MLOps concepts and best practicesExperience with core technologies such as Python, PySpark, SQL, Airflow, Databricks, Kedro, Dask/RAPIDS, Docker, Kubernetes, and cloud services (AWS/ ...

Azure AI, or 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.BenefitsCompetitive SalaryBonus SchemePrivate Healthcare Insurance25 Days Annual Leave + Bank HolidaysUp ...

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 ...

equivalent cloud-native AI/ML platforms (e.g. AWS SageMaker, Azure ML) 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). ...

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 ...

incident management Experience with AI/ML frameworks such as PyTorch, TensorFlow, or JAX Exposure to LLMs, RAG, Agentic AI, model serving, or MLOps environments What's on Offer? Work on innovative AI and Agentic AI solutions Hybrid working from London 12-month initial contract Inside IR35 Opportunity to contribute ...

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 ...

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 ...

TensorFlow, PyTorch, or Java-based ML libraries) Strong problem-solving and communication skillsPreferred Qualifications Experience with NLP, computer vision, or recommendation systems Familiarity with MLOps practices and model lifecycle management Knowledge of event-driven architectures (Kafka or similar) Experience with DevOps tooling and infrastructure as code Exposure to Generative ...

PyTorch, or Java-based ML libraries) Strong problem-solving and communication skills Preferred Qualifications Experience with NLP, computer vision, or recommendation systems Familiarity with MLOps practices and model lifecycle management Knowledge of event-driven architectures (Kafka or similar) Experience with DevOps tooling and infrastructure as code Exposure to Generative ...

degree or equivalent work experience.Preferred Skills:Experience with OpenAI, Anthropic, Mistral, Google/Gemini, Azure, AWS, GCP, Databricks, Snowflake, vector databases, model registries, and MLOps/LLMOps tooling.Experience working directly with clients, sales teams, or product teams to rapidly prototype solutions under ambiguous conditions.AI/ML, cloud, data engineering ...

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 ...

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). ...

operational controls).Broad experience across major cloud platforms (AWS, Azure, GCP) and their AI/GenAI services; cloud-agnostic experience is preferred.Good grasp of MLOps/LLMOps practices (e.g., CI/CD, model/LLM monitoring, controls and auditability) and how to embed them pragmatically within client delivery.Familiarity with large ...