Company Name: Neurons Lab

Job Details: 14,Locations,Remote,Senior,level

Job Url: https://builtin.com/job/gcp-cloud-engineer/7855940

Job Description: About the projectJoin Neurons Lab as a Senior GCP Cloud Engineer working on Generative AI solutions for banking clients. You'll be hands-on building production infrastructure on Google Cloud Platform while contributing to architecture design, with a strong focus on security, compliance, and operational excellence.Our Focus: Banking and Financial Services clients with stringent regulatory requirements (PCI-DSS, GDPR, MAS TRM). You'll architect and implement GenAI solutions - from RAG systems to ML platforms - while ensuring enterprise-grade security and compliance.Your Impact: Build cloud infrastructure using Terraform, Kubernetes, and Docker. Work across multiple banking GenAI projects, implementing architectures, creating reusable IaC patterns, and maintaining the highest security standards required by financial institutions.Duration: Part-time long-term engagement with project-based allocationsReporting: Direct report to Head of CloudObjectiveBuild and operate GenAI cloud infrastructure for banking clients on Google Cloud Platform:Engineering Excellence: Build production infrastructure using Terraform, deploy on Kubernetes/GKE, containerize with Docker, implement CI/CD pipelinesArchitecture Support: Contribute to architecture design, create technical specifications, and provide engineering insights during solution designClient Success: Implement secure, scalable, cost-effective solutions aligned with GCP best practices and financial regulationsKnowledge Transfer: Create reusable IaC patterns, comprehensive documentation, and operational runbooksKPIDeploy infrastructure through IaC (Terraform) with zero manual configurationCreate at least 3 reusable IaC components or architectural patterns per quarterImplement CI/CD pipelines for all projects with automated testing and deploymentDocument architecture and implementation details for knowledge sharingMaintain 95%+ uptime for production GenAI endpointsAreas of ResponsibilityCloud Engineering (70%):Build and maintain GCP infrastructure using Terraform - develop reusable modules for GenAI patternsDeploy and manage applications on GKE - Kubernetes manifests, Helm charts, container securityContainerize applications with Docker - multi-stage builds, optimization, securityDevelop Python applications: FastAPI backends, GenAI integration (RAG, LLM apps, chat interfaces)Deploy GenAI model serving: Vertex AI endpoints, containerized models on GKE, vector databasesImplement CI/CD pipelines: Cloud Build, GitHub Actions, automated testing and deploymentSecurity & compliance: IAM, VPC Service Controls, encryption, banking regulations (PCI-DSS, GDPR, MAS TRM)Cost optimization: GPU/TPU workload optimization, spot VMs, auto-scaling, monitoringManage GPU resources, ML pipelines, model performance monitoringArchitecture Support (30%):Contribute to GCP architecture design for GenAI solutions (RAG, LLM applications, ML platforms)Create technical specifications, provide cost estimates and feasibility inputParticipate in technical presentations and demosStay current with GCP AI/ML services (Vertex AI, Gemini, etc.)Skills & KnowledgeCertifications & Core Platform:Google Cloud Certified Professional Cloud Architect (REQUIRED - must be active/current)Core GCP services: GCE, GKE, Cloud Run, Vertex AI, VPC, IAM, Cloud KMS, Secret ManagerAWS Certified Solutions Architect (strong plus) - multi-cloud experience valuedMust-Have Technical Skills:Terraform (expert level) - GCP infrastructure, reusable modules, best practicesKubernetes/GKE (expert level) - deployment strategies, security, networking, HelmDocker (expert level) - containerization, multi-stage builds, optimizationPython (advanced) - OOP, async, FastAPI/Flask, GenAI libraries (LangChain, LlamaIndex)GenAI - LLMs, RAG, vector databases, prompt engineering, Vertex AIGPU/TPU management - optimization for training/inference workloadsCI/CD pipelines - Cloud Build, GitHub Actions, GitLab CILinux/UNIX administration, networking fundamentalsStrong Plus:Banking/FSI experience with compliance requirements (PCI-DSS, GDPR, MAS TRM)Multi-cloud architecture experienceModern DevOps practices and monitoring toolsCommunication:Advanced English (written and verbal)Client-facing presentations and demosTechnical documentationExperience5+ years in cloud engineering, DevOps, or solution architecture roles2+ years hands-on with GCP (GCE, GKE, Vertex AI, etc.) + AWS experience is a strong plus2+ years with Terraform for GCP - reusable modules, automation, standardization2+ years with Kubernetes (GKE preferred) and Docker - production clusters, security2+ years Python programming - APIs (FastAPI/Flask), GenAI applicationsGenAI/ML workloads (strong plus) - LLM apps, RAG systems, GPU/TPU computeBanking/FSI experience (strong plus) - financial services clients, compliance, securityQuestions for Applicants (please mention up to 5 questionsGCP Certification: Please confirm your Google Cloud Certified Professional Cloud Architect certification status (certification ID, issue date, expiration date). Is it currently active?GCP GenAI Experience: Describe a Generative AI project you built on GCP. What services did you use (Vertex AI, Gemini, etc.)? What was the architecture? How did you handle challenges like latency, cost, or accuracy?Terraform & Kubernetes on GCP: Provide examples of GCP infrastructure you've built with Terraform and deployed on GKE. How did you structure your Terraform modules? What Kubernetes patterns did you implement?Banking/FSI Experience: Do you have experience working with banking or financial services clients? If yes, describe the project, compliance requirements you addressed (PCI-DSS, GDPR, etc.), and security controls you implemented.AWS Background: What is your AWS experience level? Do you hold any AWS certifications? Describe any multi-cloud projects you've worked on.