1 to 25 of 298 GCP Jobs in the South East

GCP DevOps Engineer – (GCP, Google Cloud Platform, DevOps, CI/CD, GKE, Cloud Run, Cloud Build, Architecture, SRE, Kubernetes, Linux, Docker, Google) – Permanent – London/Hybrid Charles Simon Associates are recruiting for a GCP DevOps Engineer in London on an exclusive basis for our rapidly scaling business. Location: London/… deliver robust solutions. Driving cost optimisation and best practices across the GCP estate. What We’re Looking For: Hands-on experience with Google Cloud Platform (must have). Strong background in DevOps/SRE/Cloud Engineering. IaC expertise (Terraform preferred). Experience with Kubernetes/GKE and containerisation (Docker ...

version control (Git, Azure DevOps) beyond the very basics, ensuring reliable and maintainable codebases. Infrastructure & cloud: Familiarity with cloud platforms (Azure preferred; AWS or GCP also valuable), containerization (Docker, Kubernetes), and infrastructure-as-code tools like Terraform. Applied AI development: Experience working with LLM APIs (e.g., OpenAI) and building lightweight ...

tooling: Cursor, Windsurf, GitHub copilot • AI powered platform development: Lovable, Replit, Bolt • APIs & Microservices: RESTful APIs, GraphQL, microservice architectures • Cloud & DevOps: AWS, Azure, or GCP; Docker; CI/CD pipelines • Data Integration: Products integrating ML/AI models, databases, BI tools • UI/UX Awareness: Collaborate on interfaces, data visualisation ...

tooling: Cursor, Windsurf, GitHub copilot • AI powered platform development: Lovable, Replit, Bolt • APIs & Microservices: RESTful APIs, GraphQL, microservice architectures • Cloud & DevOps: AWS, Azure, or GCP; Docker; CI/CD pipelines • Data Integration: Products integrating ML/AI models, databases, BI tools • UI/UX Awareness: Collaborate on interfaces, data visualisation ...

tooling: Cursor, Windsurf, GitHub copilot • AI powered platform development: Lovable, Replit, Bolt • APIs & Microservices: RESTful APIs, GraphQL, microservice architectures • Cloud & DevOps: AWS, Azure, or GCP; Docker; CI/CD pipelines • Data Integration: Products integrating ML/AI models, databases, BI tools • UI/UX Awareness: Collaborate on interfaces, data visualisation ...

tooling: Cursor, Windsurf, GitHub copilot • AI powered platform development: Lovable, Replit, Bolt • APIs & Microservices: RESTful APIs, GraphQL, microservice architectures • Cloud & DevOps: AWS, Azure, or GCP; Docker; CI/CD pipelines • Data Integration: Products integrating ML/AI models, databases, BI tools • UI/UX Awareness: Collaborate on interfaces, data visualisation ...

tooling: Cursor, Windsurf, GitHub copilot • AI powered platform development: Lovable, Replit, Bolt • APIs & Microservices: RESTful APIs, GraphQL, microservice architectures • Cloud & DevOps: AWS, Azure, or GCP; Docker; CI/CD pipelines • Data Integration: Products integrating ML/AI models, databases, BI tools • UI/UX Awareness: Collaborate on interfaces, data visualisation ...

tooling: Cursor, Windsurf, GitHub copilot • AI powered platform development: Lovable, Replit, Bolt • APIs & Microservices: RESTful APIs, GraphQL, microservice architectures • Cloud & DevOps: AWS, Azure, or GCP; Docker; CI/CD pipelines • Data Integration: Products integrating ML/AI models, databases, BI tools • UI/UX Awareness: Collaborate on interfaces, data visualisation ...

tooling: Cursor, Windsurf, GitHub copilot • AI powered platform development: Lovable, Replit, Bolt • APIs & Microservices: RESTful APIs, GraphQL, microservice architectures • Cloud & DevOps: AWS, Azure, or GCP; Docker; CI/CD pipelines • Data Integration: Products integrating ML/AI models, databases, BI tools • UI/UX Awareness: Collaborate on interfaces, data visualisation ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

reliable and scalable back-end systems with an object-oriented language. Cloud-native technologies: Experience in architecting and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker), infrastructure-as-code software (e.g., Terraform), and observability platforms (e.g., Datadog or Grafana). Strong ownership mindset ...

software architecture. Hands-on expertise with Java, Spring Boot , and microservices. Strong understanding of Agile and DevOps practices. Familiarity with cloud platforms (AWS, Azure, GCP) and Infrastructure as Code. Experience with CI/CD, containerisation (Docker, Kubernetes), and automated testing. Excellent communication and stakeholder engagement skills. Desirable: Experience in consulting ...

RabbitMQ, MQTT, or similar) Experience with container orchestration and service containerisation (Kubernetes, Docker) Built and deployed services on any major cloud providers' services (Azure, GCP, AWS) AI-Native development: use AI tools such as CoPilot or Cursor a collaborator and accelerator of development. Nice to Have: Experience ...

language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data pipelines, APIs ...

language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data pipelines, APIs ...

language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data pipelines, APIs ...

language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data pipelines, APIs ...

language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (Azure Cognitive Services, AWS SageMaker, GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (Jenkins, Docker, Kubernetes, AWS). Ability to design data pipelines, APIs ...