1 to 25 of 99 MLOps Jobs in the UK excluding London

maintainable code for production grade AI solutions. \n 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. \n Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such ...

maintainable code for production grade AI solutions. \n 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. \n Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such ...

production-grade AI solutions. \n Proven experience in deploying AI/ML models to production environments, ensuring scalability, reliability, and performance. Familiarity with MLOps practices, including CI/CD pipelines, model versioning, and monitoring. \n Comprehensive experience with cloud platforms (AWS, Azure, or GCP) and cloud native AI tools, such ...

production-grade AI solutions. \n Proven experience in deploying AI/ML models to production environments, ensuring scalability, reliability, and performance. Familiarity with MLOps practices, including CI/CD pipelines, model versioning, and monitoring. \n Comprehensive experience with cloud platforms (AWS, Azure, or GCP) and cloud native AI tools, such ...

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

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 managementKnowledge of event-driven architectures (Kafka or similar)Experience with DevOps tooling and infrastructure as codeExposure to Generative AI and large ...

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). ...

maintainable code for production-grade AI solutions.\ NProven experience indeploying AI/ML models to production environments, ensuring scalability, reliability, and performance. Familiarity with MLOps practices, including CI/CD pipelines, model versioning, and monitoring.\ NComprehensive experience with cloud platforms (AWS, Azure, or GCP) and cloud native AI tools, such ...

statistics, optimization and ML theory. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.)Specialism or well-researched interest in NLPBroad knowledge of MLOps tooling - for versioning, reproducibility, observability etc.Experience monitoring, maintaining, enhancing existing models over an extended time periodExtensive experience with pytorch and related data science python libraries ...

engineering, fine-tuning, RAG) LangChain/LangGraph Agent frameworks Vector databases FastAPI/APIs AWS/Azure/GCP/Databricks CI/CD, MLOps/LLMOps What you'll bring: - Strong software or data engineering foundations - Experience delivering AI or AI-enabled solutions - Exposure to cloud and modern data ...

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 ...

engineering, fine-tuning, RAG) · LangChain/LangGraph · Agent frameworks · Vector databases · FastAPI/APIs · AWS/Azure/GCP/Databricks · CI/CD, MLOps/LLMOps What you'll bring: - Strong software or data engineering foundations - Experience delivering AI or AI-enabled solutions - Exposure to cloud and modern data ...

Computer Science, Data Science, Machine Learning, or related field. Proven experience building and deploying AI applications in large-scale production environments.Experience with MLOps practices and tools for managing the machine learning lifecycle.Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn.Strong understanding of data preprocessing, feature ...

professional accreditation such as CEng. Experience with graph databases and advanced query languages (e.g., Neo4j, Gremlin). Knowledge of machine learning data pipelines and MLOps practices. Familiarity with data virtualisation and data mesh concepts. Hands-on experience with containerisation and orchestration (Docker, Kubernetes, Red Hat OpenShift and Ceph). Exposure ...

code tools, and containerization/orchestration tools (Docker, Kubernetes) to operate, support, and secure mission-critical applications Preferred qualifications, capabilities and skills Experience with MLOps tools and platforms such as MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, and Kubeflow Exposure to cloud-native microservices architecture Familiarity with advanced ...

data science solutions. Ensure sharing best practices and learnings with other squad members. Design and spearhead the development of Gen AI, AI Agents and MLOps platforms, applications, and solutions driven by GenAI, deep learning and machine learning to address business challenges and elevate product performance. Build standards and recommendations ...

Predictive modelling and machine learning Building production-ready AI applications Communicating technical concepts to both technical and non-technical stakeholders Experience with Microsoft Fabric, MLOps, compliance analytics or risk modelling would be a bonus, but isn't essential. Why This Role? This is an opportunity to join a business where ...

members and providing technical leadership. Desirable Skills & Experience MSc or PhD with a strong research background. Experience developing and maintaining robust ML pipelines and MLOps practices. Familiarity with version control, containerisation and environment management tools, including Git and Docker. Experience working within Linux environments and using command-line tooling. Experience ...

CEng.\n \n \nExperience with graph databases and advanced query languages (e.g., Neo4j, Gremlin).\n \n \nKnowledge of machine learning data pipelines and MLOps practices.\n \n \nFamiliarity with data virtualisation and data mesh concepts.\n \n \nHands-on experience with containerisation and orchestration (Docker, Kubernetes, Red Hat OpenShift ...

Learning Applied machine learning, computer vision, object detection, image classification, model development and evaluation, data annotation and curation, training pipelines, performance optimisation, edge ML, MLOps, deployment and maintenance of ML solutions, Python development, Python-based ML frameworks, PyTorch, TensorFlow, sklearn, OpenCV, NumPy, feature development, code review, pytest, linting, bug fixing ...

deployments CI/CD experience with a focus on production ownership and reliability Understanding of data quality, testing and lineage best practices Desirable skills: MLOps or production machine learning experience Terraform, Ansible or other Infrastructure as Code tools PostgreSQL and modern data platforms Experience within Defence, National Security or regulated ...

experience) Desirable Experience Previous experience within aviation, airlines or travel technology environments Experience with AI automation tooling, prompt engineering and LLM implementation Exposure to MLOps and production AI environments Understanding of responsible AI, governance and compliance considerations Experience contributing to technical thought leadership, industry events or customer presentations Exposure ...

analytics alongside AI solutions). Technical Scope Platform‐agnostic engineering mindset; able to work across varied client stacks. Experience with cloud AI services and MLOps/LLMOps (e.g., Azure AI Foundry, AWS SageMaker and/or Bedrock). Strong software engineering practices: Python, APIs, containerisation, CI/CD, IaC, testing ...

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 ...

maintain ML solutions on modern frameworks to meet functional business requirements, adhering to software engineering best practices and with exposure to version control, testing, MLOps, CI/CD and API designHands on experience in using one of 3 major cloud technologies (AWS, Azure or GCP) in a production environment ...