1 to 25 of 178 GCP Jobs in the South West

proficiency in one or more programming languages (e.g., Python, Java, JavaScript) and experience with software architecture patterns. Experience with cloud platforms (e.g., AWS, Azure, GCP) and infrastructure as code. Familiarity with CI/CD pipelines, automated testing, and modern software development practices. Knowledge of technologies such as microservices, containerisation (Docker ...

Solid experience with RESTful APIs, microservices, authentication/authorization, and database design (SQL/NoSQL). Strong understanding of cloud platforms (AWS, Azure, or GCP). Experience mentoring junior developers or leading small development teams. Familiarity with AI/ML frameworks, data pipelines, or integrating large language models/APIs. ...

multiple programming languages such as Python, JavaScript, or similar. Extensive experience writing well-tested code. Good knowledge of cloud platforms (e.g., AWS, Azure, GCP) and infrastructure as code Experience with CI/CD pipelines and automated testing frameworks. Experience with various architecture patterns eg microservices and event-driven. Experience with ...

GCP DevOps Engineer - AI Infrastructure (Financial Services) Location: London Contract Type: Full-time/Permanent 2 days required at Office and 3 from home per week £(Apply online only) DOE Join a forward-thinking financial services firm leveraging AI to transform decision-making and customer experiences. We're seeking … skilled GCP DevOps Engineer to architect and maintain cloud-native infrastructure that supports high-performance AI systems. This is a hands-on technical role with strategic impact across infrastructure, automation, and security. Key Responsibilities Infrastructure & Cloud Engineering Design, build, and manage scalable, resilient infrastructure on Google Cloud Platform (GCP) . ...

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 ...

proficiency in one or more programming languages (e.g., Python, Java, JavaScript) and experience with software architecture patterns. Experience with cloud platforms (e.g., AWS, Azure, GCP) and infrastructure as code. Familiarity with CI/CD pipelines, automated testing, and modern software development practices. Knowledge of technologies such as microservices, containerisation (Docker ...

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 ...

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 ...

scale, mission-critical systems. Strong hands-on experience with Kubernetes and Terraform . Experience deploying and operating applications in public cloud environments (AWS, Azure, GCP) . Solid understanding of Java and Spring Boot applications . Experience with monitoring, logging, and observability tools (Prometheus, Grafana, ELK, Splunk). Strong troubleshooting ...

About You Professional knowledge and experience Essential Proven experience in data engineering, data integration and data modelling Expertise with cloud platforms (e.g. AWS, Azure, GCP) Expertise with modern cloud data platforms (e.g. Microsoft Fabric, Databricks) Expertise with multiple data analytics tools (e.g. Power BI) Deep understanding of data wareho using ...

techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across ...

techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across ...

techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across ...

techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across ...

techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across ...

techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices. Familiarity with higher education processes and integrations across ...