76 to 100 of 339 Containerisation Jobs in the South East

products Basic qualifications: Extensive experience conducting Bankend Development using Python. Concurrency and Asyn exp Demonstrated experience using FastAPI Cloud Experience - GCP Essential SQL Docker Containerization Unit testing experience - TDD or Test Suit ML or AI experience would be desriable. Experience within Life Sceiences or Pharma Sector If you are available ...

Playwright) and observability to deliver resilient, maintainable, and production-ready business logic. Drive DevOps excellence by collaborating on CI/CD pipelines (Jenkins, Concourse), containerisation (Docker) and Kubernetes deployments. Mentor and empower fellow engineers through thoughtful code reviews, technical guidance and fostering a culture of collaborative problem-solving. Influence technical ...

brownfield rollout scenarios. Knowledge of software testing methodologies for infrastructure automation. Experience with other programming languages (e.g., Go, PowerShell, Bash). Understanding of containerization and cloud-native patterns. Experience working in high-pressure, global teams on multi-year strategic projects. Personal Attributes: Self-motivated and able to operate independently with ...

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 ...

Working with 3D geometry or FEA/CFD simulation tools Cloud infrastructure (AWS, Azure) and tools like Terraform CI/CD pipelines and containerisation Machine learning model development or agentic AI workflows Infrastructure automation and scalable deployment systems Mechanical design principles and turbomachinery fundamentals Ideal for someone with ...

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 ...