51 to 75 of 230 Containerisation Jobs in the Midlands

testing Ability to work collaboratively in a team environment Desirables Azure cloud experience (App Services, Functions, Storage, Service Bus, etc.) Exposure to microservices or containerisation (Docker/Kubernetes) Experience with front-end frameworks such as React or Vue The Company This company is a recognised leader in the financial services ...

Automate model training, testing, deployment, and monitoring workflows. 5. Cloud & Infrastructure Build ML solutions on cloud platforms such as AWS, Azure, or GCP. Use containerization and orchestration tools (Docker, Kubernetes). Optimize compute costs and performance for training and inference workloads. 6. Collaboration & Stakeholder Engagement Work closely with Data Scientists ...

Automate model training, testing, deployment, and monitoring workflows. 5. Cloud & Infrastructure Build ML solutions on cloud platforms such as AWS, Azure, or GCP. Use containerization and orchestration tools (Docker, Kubernetes). Optimize compute costs and performance for training and inference workloads. 6. Collaboration & Stakeholder Engagement Work closely with Data Scientists ...

Automate model training, testing, deployment, and monitoring workflows. 5. Cloud & Infrastructure Build ML solutions on cloud platforms such as AWS, Azure, or GCP. Use containerization and orchestration tools (Docker, Kubernetes). Optimize compute costs and performance for training and inference workloads. 6. Collaboration & Stakeholder Engagement Work closely with Data Scientists ...

Automate model training, testing, deployment, and monitoring workflows. 5. Cloud & Infrastructure Build ML solutions on cloud platforms such as AWS, Azure, or GCP. Use containerization and orchestration tools (Docker, Kubernetes). Optimize compute costs and performance for training and inference workloads. 6. Collaboration & Stakeholder Engagement Work closely with Data Scientists ...

Automate model training, testing, deployment, and monitoring workflows. 5. Cloud & Infrastructure Build ML solutions on cloud platforms such as AWS, Azure, or GCP. Use containerization and orchestration tools (Docker, Kubernetes). Optimize compute costs and performance for training and inference workloads. 6. Collaboration & Stakeholder Engagement Work closely with Data Scientists ...

Automate model training, testing, deployment, and monitoring workflows. 5. Cloud & Infrastructure Build ML solutions on cloud platforms such as AWS, Azure, or GCP. Use containerization and orchestration tools (Docker, Kubernetes). Optimize compute costs and performance for training and inference workloads. 6. Collaboration & Stakeholder Engagement Work closely with Data Scientists ...

Automate model training, testing, deployment, and monitoring workflows. 5. Cloud & Infrastructure Build ML solutions on cloud platforms such as AWS, Azure, or GCP. Use containerization and orchestration tools (Docker, Kubernetes). Optimize compute costs and performance for training and inference workloads. 6. Collaboration & Stakeholder Engagement Work closely with Data Scientists ...

Automate model training, testing, deployment, and monitoring workflows. 5. Cloud & Infrastructure Build ML solutions on cloud platforms such as AWS, Azure, or GCP. Use containerization and orchestration tools (Docker, Kubernetes). Optimize compute costs and performance for training and inference workloads. 6. Collaboration & Stakeholder Engagement Work closely with Data Scientists ...

Automate model training, testing, deployment, and monitoring workflows. 5. Cloud & Infrastructure Build ML solutions on cloud platforms such as AWS, Azure, or GCP. Use containerization and orchestration tools (Docker, Kubernetes). Optimize compute costs and performance for training and inference workloads. 6. Collaboration & Stakeholder Engagement Work closely with Data Scientists ...

Automate model training, testing, deployment, and monitoring workflows. 5. Cloud & Infrastructure Build ML solutions on cloud platforms such as AWS, Azure, or GCP. Use containerization and orchestration tools (Docker, Kubernetes). Optimize compute costs and performance for training and inference workloads. 6. Collaboration & Stakeholder Engagement Work closely with Data Scientists ...

pipelines for near-real-time detection (packet/flow analytics). Background in resilience engineering, chaos testing, disaster recovery, and capacity planning at hyperscale. Containerization/orchestration (e.g., Kubernetes) and secure service-to-service communication (mTLS, policy enforcement). Familiarity with zero trust, segmentation, and modern security architectures; exposure ...

pipelines for near-real-time detection (packet/flow analytics). Background in resilience engineering, chaos testing, disaster recovery, and capacity planning at hyperscale. Containerization/orchestration (e.g., Kubernetes) and secure service-to-service communication (mTLS, policy enforcement). Familiarity with zero trust, segmentation, and modern security architectures; exposure ...

pipelines for near-real-time detection (packet/flow analytics). Background in resilience engineering, chaos testing, disaster recovery, and capacity planning at hyperscale. Containerization/orchestration (e.g., Kubernetes) and secure service-to-service communication (mTLS, policy enforcement). Familiarity with zero trust, segmentation, and modern security architectures; exposure ...

pipelines for near-real-time detection (packet/flow analytics). Background in resilience engineering, chaos testing, disaster recovery, and capacity planning at hyperscale. Containerization/orchestration (e.g., Kubernetes) and secure service-to-service communication (mTLS, policy enforcement). Familiarity with zero trust, segmentation, and modern security architectures; exposure ...

pipelines for near-real-time detection (packet/flow analytics). Background in resilience engineering, chaos testing, disaster recovery, and capacity planning at hyperscale. Containerization/orchestration (e.g., Kubernetes) and secure service-to-service communication (mTLS, policy enforcement). Familiarity with zero trust, segmentation, and modern security architectures; exposure ...

pipelines for near-real-time detection (packet/flow analytics). Background in resilience engineering, chaos testing, disaster recovery, and capacity planning at hyperscale. Containerization/orchestration (e.g., Kubernetes) and secure service-to-service communication (mTLS, policy enforcement). Familiarity with zero trust, segmentation, and modern security architectures; exposure ...

pipelines for near-real-time detection (packet/flow analytics). Background in resilience engineering, chaos testing, disaster recovery, and capacity planning at hyperscale. Containerization/orchestration (e.g., Kubernetes) and secure service-to-service communication (mTLS, policy enforcement). Familiarity with zero trust, segmentation, and modern security architectures; exposure ...

pipelines for near-real-time detection (packet/flow analytics). Background in resilience engineering, chaos testing, disaster recovery, and capacity planning at hyperscale. Containerization/orchestration (e.g., Kubernetes) and secure service-to-service communication (mTLS, policy enforcement). Familiarity with zero trust, segmentation, and modern security architectures; exposure ...

pipelines for near-real-time detection (packet/flow analytics). Background in resilience engineering, chaos testing, disaster recovery, and capacity planning at hyperscale. Containerization/orchestration (e.g., Kubernetes) and secure service-to-service communication (mTLS, policy enforcement). Familiarity with zero trust, segmentation, and modern security architectures; exposure ...

pipelines for near-real-time detection (packet/flow analytics). Background in resilience engineering, chaos testing, disaster recovery, and capacity planning at hyperscale. Containerization/orchestration (e.g., Kubernetes) and secure service-to-service communication (mTLS, policy enforcement). Familiarity with zero trust, segmentation, and modern security architectures; exposure ...

pipelines for near-real-time detection (packet/flow analytics). Background in resilience engineering, chaos testing, disaster recovery, and capacity planning at hyperscale. Containerization/orchestration (e.g., Kubernetes) and secure service-to-service communication (mTLS, policy enforcement). Familiarity with zero trust, segmentation, and modern security architectures; exposure ...

pipelines for near-real-time detection (packet/flow analytics). Background in resilience engineering, chaos testing, disaster recovery, and capacity planning at hyperscale. Containerization/orchestration (e.g., Kubernetes) and secure service-to-service communication (mTLS, policy enforcement). Familiarity with zero trust, segmentation, and modern security architectures; exposure ...

Syslog, CEF Infrastructure & Networking Linux and Windows server administration Networking fundamentals (TCP/IP, DNS, firewalls, proxies) Cloud platforms: AWS, Azure, GCP Containerization: Docker, Kubernetes Identity & Access Management SSO protocols: SAML, OAuth, OpenID Connect MFA solutions: Duo, Okta, Azure MFA LDAP/Active Directory integration RBAC design and implementation Data ...

Server, including schema design, query optimisation, and performance tuning. DevOps & Cloud Skills: Practical experience with CI/CD tools (Azure DevOps, GitHub Actions) and containerisation (Docker). Familiarity with Azure services (Functions, App Services, Storage, Key Vault, Azure SQL). Testing & Quality: Proficiency in unit testing frameworks (MSTest, xUnit ...