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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.