101 to 125 of 417 Containerisation Jobs in the South East

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 ...

analytical skills. Excellent communication and teamwork skills. Nice-to-Have: Experience with CI/CD pipelines using tools like GitHub Actions. Experience with containerization (eg, Docker). Familiarity with serverless architectures. Experience with SonarCloud or Snyk for code quality and security. Familiarity with TurboRepo for managing monorepos. Knowledge of Storybook ...

tolerance, disaster recovery, and enterprise-grade resilience. Migration Planning & Execution Assess existing infrastructure, applications, and workloads for migration readiness. Develop comprehensive migration strategies covering containerization, workload transfer, and data migration. Implement migration processes with minimal downtime and operational disruption. Identify risks and implement mitigation strategies for smooth migration. VMware & Pure ...

applications. Knowledge of vector databases (FAISS, Pinecone, Weaviate) and graph technologies (Neo4j). Familiarity with observability tools and AI evaluation frameworks. Strong grasp of containerization and cloud deployment (Docker, AWS, Azure). Tools & Platforms GitHub Copilot, ChatGPT/GPT-4, Claude for AI-assisted development. LangChain, LlamaIndex for RAG implementations. ...

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 ...

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 ...