1 to 25 of 228 Permanent MLOps Jobs in the UK

rapidly evolving AI technologies Desirable Experience Voice AI solutions Speech Recognition technologies Accessibility-focused AI applications Agentic AI systems Multi-modal AI platforms MLOps and model monitoring Real-time AI inference systems Enterprise AI deployments Education & Experience Degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Engineering or related discipline ...

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 ...

agent architecture design, state management, and graph-based workflow orchestration). ML & Cloud Ecosystems: Solid understanding of ML algorithms, FastAPI, PyTorch/TensorFlow, MLflow, MLOps practices, containerization (Docker, Kubernetes/AWS EKS), and cloud services (AWS Bedrock, SageMaker, Azure AI Search). Soft Skills: Excellent communication and collaboration skills, with ...

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 ...

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

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

product stacks. \n\n Machine Learning Engineering: \n \n End-to-end ML lifecycle expertise: feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. \n\n \n Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ...

product stacks. \n\n Machine Learning Engineering: \n \n End-to-end ML lifecycle expertise: feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. \n\n \n Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ...

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 ...

theory. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.) \n Specialism or well-researched interest in NLP \n Broad knowledge of MLOps tooling - for versioning, reproducibility, observability etc. \n Experience monitoring, maintaining, enhancing existing models over an extended time period \n Extensive experience with pytorch and related data ...

theory. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.) \n Specialism or well-researched interest in NLP \n Broad knowledge of MLOps tooling - for versioning, reproducibility, observability etc. \n Experience monitoring, maintaining, enhancing existing models over an extended time period \n Extensive experience with pytorch and related data ...

Java-based ML libraries)\ NStrong problem-solving and communication skills\ N\nPreferred Qualifications\ N\nExperience with NLP, computer vision, or recommendation systems\ NFamiliarity with MLOps practices and model lifecycle management\ NKnowledge of event-driven architectures (Kafka or similar)\ NExperience with DevOps tooling and infrastructure as code\ NExposure to Generative ...

product stacks. \ N\nMachine Learning Engineering: \ N\nEnd-to-end ML lifecycle expertise: feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. \ N\n\nPractical knowledge of ML frameworks (e.G. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). ...

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 ...

including LangChain and LangGraph, agent frameworks, vector databases, FastAPI, and cloud platforms such as AWS, Azure, GCP or Databricks. Familiarity with CI/CD, MLOps and LLMOps practices is also expected. Strong communication skills and the ability to engage effectively with stakeholders across technical and business functions are important ...

including LangChain and LangGraph, agent frameworks, vector databases, FastAPI, and cloud platforms such as AWS, Azure, GCP or Databricks. Familiarity with CI/CD, MLOps and LLMOps practices is also expected. Strong communication skills and the ability to engage effectively with stakeholders across technical and business functions are important ...

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 experience designing enterprise AI or ML architectures, with ...

similar interoperability patterns Monitor, troubleshoot, and continuously improve models and AI workflows in production to ensure reliability, quality, and accuracy Apply LLMOps and MLOps best practices across experimentation, versioning, deployment, monitoring, and lifecycle management Ensure AI/ML solutions align with cloud governance, security, compliance, and responsible AI requirements Document ...

reliability, incident management, and continuous improvement. Proficiency in Python software engineering, including production-grade, modular OOP design, testing, performance tuning, and debugging. Familiarity with MLOps and distributed systems, including training and serving patterns, batch and real-time architectures, feature stores, orchestration, and scalable data processing. Ability to design evaluations aligned ...

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