126 to 150 of 696 MLOps Jobs in England

Generative AI.Agent frameworks and orchestration technologies.Retrieval Augmented Generation (RAG) architectures.API development and microservice design.Cloud-native development (Azure preferred).CI/CD automation and MLOps practices.Vector databases and knowledge retrieval platforms.Data modelling and distributed systems design.AI observability, evaluation and governance frameworks.Desirable ExperienceExperience within Financial Services or highly regulated environments.Knowledge of Service ...

orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool‐use patterns, human‐in‐the‐loop workflows, and agentic safety at enterprise scale. AI/ML Engineering & MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using platforms such ...

orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool-use patterns, human-in-the-loop workflows, and agentic safety at enterprise scale* AI/ML Engineering & MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using platforms such ...

orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool-use patterns, human-in-the-loop workflows, and agentic safety at enterprise scaleAI/ML Engineering & MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using platforms such ...

modern software development 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. ...

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/ ...

coursework.Communication & Collaboration: Strong verbal and written communication skills, with the ability to work effectively in a team environment and learn from senior members.Desirable experienceCloud & MLOps Exposure: Basic familiarity with a cloud platform (AWS, GCP, or Azure) and an awareness of concepts like containerization (Docker).Core ML Knowledge: A good grasp ...

machine learning fundamentals, statistics, and working with complex datasets to support model development, training, fine‐tuning, and inference. Experience with CI/CD, containerisation, MLOps/LLMOps workflows, and monitoring AI models in production, including evaluating performance, safety, and cost efficiency. Ability to translate complex business challenges into effective ...

machine learning fundamentals, statistics, and working with complex datasets to support model development, training, fine-tuning, and inference. Experience with CI/CD, containerisation, MLOps/LLMOps workflows, and monitoring AI models in production, including evaluating performance, safety, and cost efficiency. Ability to translate complex business challenges into effective ...

Specialist Team). The impact you will have Lead the architectural design of production‐grade ML workloads on our unified platform, encompassing the entire MLOps lifecycle from end‐to‐end pipeline creation and optimization (training/inference) to seamless integration with cloud‐native services. Provide advanced technical support ...

projects and deployments of AI systems. Provide guidance and mentoring to other team members on best practices in AI engineering. Use best practices (e.g., MLOps, AIOps) to improve products/services and processes related to AI. Optimise existing model serving and data pipelines to meet changing performance and security requirements. ...

Specialist Team). The impact you will have Lead the architectural design of production-grade ML workloads on our unified platform, encompassing the entire MLOps lifecycle from end-to-end pipeline creation and optimization (training/inference) to seamless integration with cloud-native services. Provide advanced technical support ...

facing digital products, ideally in banking, fintech, or other regulated industries. Exposure to AI/ML platforms, model-serving environments, GPU-enabled infrastructure, or MLOps practices. Familiarity with security, compliance, and governance expectations in enterprise software environments. Experience contributing to platform modernization, developer experience initiatives, or multi-product engineering environments. ...

equivalent work experience. Preferred Skills: Experience with Azure, AWS, GCP, OpenAI, Anthropic, Mistral, Google/Gemini, Databricks, Snowflake, vector databases, orchestration frameworks, and MLOps/LLMOps tooling.Experience supporting large pursuits, proposal development, pricing reviews, or delivery mobilization.AI/ML, cloud, data platform, architecture, or security certifications.Strong analytical thinking, commercial judgment ...

least oneDeep experience with distributed computing with Apache Spark and knowledge of Spark runtime internalsFamiliarity with CI/CD for production deploymentsWorking knowledge of MLOps, ML/AI models and AI APIsDesign and deployment of performant production end-to-end data architectures and applications that combine data pipelines, ML/ ...

controlStrong collaboration skills, working across engineering, security, data science and product teamsAnd it would be great if you had any of the following...Exposure to MLOps or SMLOps tooling and model lifecycle managementExperience supporting AI/ML pipelines, feature engineering or analytics platformsKnowledge of AI governance and secure adoption of generative ...

controlStrong collaboration skills, working across engineering, security, data science and product teamsAnd it would be great if you had any of the following...Exposure to MLOps or SMLOps tooling and model lifecycle managementExperience supporting AI/ML pipelines, feature engineering or analytics platformsKnowledge of AI governance and secure adoption of generative ...

such as PyTorch or TensorFlow. Industry experience in researching or applying machine learning, especially if in recommender systems, ranking or personalisation Good understanding of MLOps and infrastructure concepts: CI/CD for ML, feature stores, model serving, observability, and versioning. Familiarity with containerisation and cloud‐native environments (e.g. Docker, Kubernetes ...

technical design and architecture for data‐science‐enabled applications, ensuring solutions are maintainable, supportable, observable and easy to operate. Productionise delivery with DevOps and MLOps, automating deployment in AWS and putting evaluation, monitoring and provider‐routing controls in place so AI outputs remain reliable and cost‐effective. Scale practical ...

Deep knowledge of LLM APIs, prompt engineering, and conversational AI patterns. Experience in fine-tuning LLMs for domain-specific enterprise applications. Strong expertise in MLOps and LLMOps, ensuring scalable, reliable, and monitorable model deployments. Experience with agentic frameworks and autonomous agent architectures. Proficiency in Python and modern software development practices ...

framework such as PyTorch or TensorFlow.Industry experience in researching or applying machine learning, especially if in recommender systems, ranking or personalisationGood understanding of MLOps and infrastructure concepts: CI/CD for ML, feature stores, model serving, observability, and versioning.Familiarity with containerisation and cloud-native environments (e.g. Docker, Kubernetes, GCP).Familiarity ...

production environments.Deep knowledge of LLM APIs, prompt engineering, and conversational AI patterns.Experience in fine-tuning LLMs for domain-specific enterprise applications.Strong expertise in MLOps and LLMOps, ensuring scalable, reliable, and monitorable model deployments.Experience with agentic frameworks and autonomous agent architectures.Proficiency in Python and modern software development practices (testing, code review ...

customers and internal stakeholders.Excellent coding practice using Python and associated ML packages (HuggingFace, TensorFlow, PyTorch), following best practices.Experience developing robust ML pipelines with MLOps tools for model training, monitoring and deployment.Experience of appropriate version control and environment management (such as venvs or Docker).Experience of writing technical project proposals.Experience ...

aspects of generative AI, collaborating with digital, platform, and governance stakeholders.Deploy and Scale AI SolutionsDrive model deployment activities, including containerization, testing, performance monitoring, and MLOps processes.Help utilise cloud platforms (AWS, Azure, GCP) for scalable data science and generative AI deployments.Work with engineers to support the deployment of machine‐learning algorithms ...

support the following: Digital sovereignty Compute optimisation Cost optimisation This role will touch on many elements, including data platforms, AI architecture, cloud architecture, MLOPs, integration frameworks, DevOps processes. You will work closely with the BA and Lead Consultant to make strategic recommendations. With the As-Is architecture you will work ...