GCP Cloud Engineer

About The Project
Join 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 allocations

Reporting:
Direct report to Head of Cloud

Objective

Build 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 pipelines
• Architecture Support: Contribute to architecture design, create technical specifications, and provide engineering insights during solution design
• Client Success: Implement secure, scalable, cost-effective solutions aligned with GCP best practices and financial regulations
• Knowledge Transfer: Create reusable IaC patterns, comprehensive documentation, and operational runbooks

KPI

• Deploy infrastructure through IaC (Terraform) with zero manual configuration
• Create at least 3 reusable IaC components or architectural patterns per quarter
• Implement CI/CD pipelines for all projects with automated testing and deployment
• Document architecture and implementation details for knowledge sharing
• Maintain 95%+ uptime for production GenAI endpoints

Areas of Responsibility

Cloud Engineering (70%):

• Build and maintain GCP infrastructure using Terraform - develop reusable modules for GenAI patterns
• Deploy and manage applications on GKE - Kubernetes manifests, Helm charts, container security
• Containerize applications with Docker - multi-stage builds, optimization, security
• Develop Python applications: FastAPI backends, GenAI integration (RAG, LLM apps, chat interfaces)
• Deploy GenAI model serving: Vertex AI endpoints, containerized models on GKE, vector databases
• Implement CI/CD pipelines: Cloud Build, GitHub Actions, automated testing and deployment
• Security & compliance: IAM, VPC Service Controls, encryption, banking regulations (PCI-DSS, GDPR, MAS TRM)
• Cost optimization: GPU/TPU workload optimization, spot VMs, auto-scaling, monitoring
• Manage GPU resources, ML pipelines, model performance monitoring

Architecture Support (30%):

• Contribute to GCP architecture design for GenAI solutions (RAG, LLM applications, ML platforms)
• Create technical specifications, provide cost estimates and feasibility input
• Participate in technical presentations and demos
• Stay current with GCP AI/ML services (Vertex AI, Gemini, etc.)

Skills & Knowledge
Certifications & 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 Manager
• AWS Certified Solutions Architect (strong plus) - multi-cloud experience valued

Must-Have Technical Skills:

• Terraform (expert level) - GCP infrastructure, reusable modules, best practices
• Kubernetes/GKE (expert level) - deployment strategies, security, networking, Helm
• Docker (expert level) - containerization, multi-stage builds, optimization
• Python (advanced) - OOP, async, FastAPI/Flask, GenAI libraries (LangChain, LlamaIndex)
• GenAI - LLMs, RAG, vector databases, prompt engineering, Vertex AI
• GPU/TPU management - optimization for training/inference workloads
• CI/CD pipelines - Cloud Build, GitHub Actions, GitLab CI
• Linux/UNIX administration, networking fundamentals

Strong Plus:

• Banking/FSI experience with compliance requirements (PCI-DSS, GDPR, MAS TRM)
• Multi-cloud architecture experience
• Modern DevOps practices and monitoring tools

Communication:

• Advanced English (written and verbal)
• Client-facing presentations and demos
• Technical documentation

Experience

• 5+ years in cloud engineering, DevOps, or solution architecture roles
• 2+ years hands-on with GCP (GCE, GKE, Vertex AI, etc.) + AWS experience is a strong plus
• 2+ years with Terraform for GCP - reusable modules, automation, standardization
• 2+ years with Kubernetes (GKE preferred) and Docker - production clusters, security
• 2+ years Python programming - APIs (FastAPI/Flask), GenAI applications
• GenAI/ML workloads (strong plus) - LLM apps, RAG systems, GPU/TPU compute
• Banking/FSI experience (strong plus) - financial services clients, compliance, security

Questions for Applicants (please mention up to 5 questions

• GCP 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.