1 to 25 of 740 Permanent MLOps Jobs in England

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Statistics. Proven track record of delivering data science projects, especially in enterprise software or SaaS environments. Basic familiarity with CI/CD pipelines and MLOps practices, including automated testing, model versioning, and monitoring workflows. Hands-on experience with containerisation and orchestration technologies (e.g., Docker, Kubernetes) to support AI/ ...

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. BenefitsCompetitive SalaryBonus SchemePrivate Healthcare Insurance25 Days Annual Leave + Bank HolidaysUp ...