1 to 25 of 878 MLOps Jobs in the UK

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 ...

guiding teams in the effective use of these tools. Extensive expertise in designing, deploying, and maintaining production-grade AI/ML solutions, including pipelines, MLOps practices (e.g., CI/CD pipelines, model versioning, monitoring), and seamless integration with enterprise systems such as Workday. Extensive experience designing and implementing generative ...

guiding teams in the effective use of these tools. Extensive expertise in designing, deploying, and maintaining production-grade AI/ML solutions, including pipelines, MLOps practices (e.g., CI/CD pipelines, model versioning, monitoring), and seamless integration with enterprise systems such as Workday. Extensive experience designing and implementing generative ...

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

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

. 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 ...

validation and monitoring frameworks to ensure AI model performance, reliability and ongoing optimisation. You’ll establish and promote best practices for AI data pipelines, MLOps/AIOps, cloud infrastructure alignment and scalable architecture design. You’ll collaborate with Data Science, Data Engineering, Software Engineering and Enterprise Architecture teams to deliver ...

validation and monitoring frameworks to ensure AI model performance, reliability and ongoing optimisation. You'll establish and promote best practices for AI data pipelines, MLOps/AIOps, cloud infrastructure alignment and scalable architecture design. You'll collaborate with Data Science, Data Engineering, Software Engineering and Enterprise Architecture teams to deliver ...

testing, validation and monitoring frameworks to ensure AI model performance, reliability and ongoing optimisation.You’ll establish and promote best practices for AI data pipelines, MLOps/AIOps, cloud infrastructure alignment and scalable architecture design.You’ll collaborate with Data Science, Data Engineering, Software Engineering and Enterprise Architecture teams to deliver ...

/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 ...

.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 ...

.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 ...

.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 ...

.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 ...

.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 ...

APIs. Knowledge of cloud-native AI deployments. Experience with containerisation technologies such as Docker and Kubernetes. Familiarity with CI/CD pipelines and MLOps practices. Experience working with vector databases, Retrieval-Augmented Generation (RAG), embeddings and semantic search. Experience with cloud AI platforms such as Azure OpenAI, AWS Bedrock ...

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

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

code for production-grade AI solutions. 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. Comprehensive experience with cloud platforms (AWS, Azure, or GCP) and cloud native AI tools, such ...

code for production-grade AI solutions. 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. Comprehensive experience with cloud platforms (AWS, Azure, or GCP) and cloud native AI tools, such ...

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 ...