Senior Cloud Engineer

Role Overview

We are looking for a Senior Cloud Engineer who can design, build, and scale complex cloud and hybrid infrastructure from scratch (0 → production → scale).

This role is not limited to cloud provisioning. You will own:

• Multi-cloud architecture (Azure, AWS, GCP) 

• End-to-end DevSecOps and MLOps platforms 

• Inference and training infrastructure 

• Robotics/edge deployment pipelines (OTA) 

• Scalable SaaS platform architecture 

• Hybrid observability and monitoring systems 

Key Responsibilities

1. Cloud Architecture & Platform Engineering

• Architect and implement multi-cloud environments (AWS, Azure, GCP) across IaaS, PaaS, and SaaS workloads 

• Design landing zones, networking (VPC/VNet), IAM, storage, and security baselines 

• Build highly available, fault-tolerant systems across regions and clouds 

• Optimize for cost, performance, and scalability, aligned with Well-Architected Frameworks 

• Manage cloud subscriptions/accounts and governance 

• Implement Cloud Security Posture Management (CSPM) 

2. DevSecOps & Platform Automation

• Design and implement secure CI/CD pipelines (GitOps preferred) 

• Integrate: 

• SAST, DAST, container scanning, IaC scanning, VAPT 
• Secrets management (Vault, KMS, etc.) 

• Enforce policy-as-code (OPA, Azure Policy, AWS SCPs) 

• Automate infrastructure provisioning using Terraform (mandatory) 

• Implement vulnerability scanning and patch management processes 

3. MLOps & AI Infrastructure

• Build and manage: 

• Model training pipelines 
• Inference clusters (real-time and batch) 

• Deploy models using: 

• Kubernetes (AKS/EKS/GKE) 
• Serverless or GPU-based inference systems 

• Implement: 

• Model versioning 
• Experiment tracking 
• CI/CD for ML workflows 

• Optimize GPU utilization and cost efficiency 

• Experience with data annotation tools and workflows 

• Hands-on exposure to: 

• Azure AI / AI Foundry 
• AWS SageMaker 
• GCP Vertex AI 

4. Robotics DevOps & OTA Systems

• Design pipelines for robotics and edge device deployments 

• Implement OTA (Over-the-Air) update systems 

• Handle: 

• Intermittent connectivity 
• Edge-to-cloud synchronization 

• Work with: 

• ROS/ROS2 environments (preferred) 
• Containerized edge workloads 

5. SaaS Platform Architecture

• Architect and deploy multi-tenant SaaS platforms 

• Implement: 

• Tenant isolation 
• Scalable backend services 
• API gateways and service meshes 

• Ensure high availability and zero-downtime deployments 

6. Hybrid Infrastructure (Cloud + On-Prem)

• Design and manage: 

• On-prem compute clusters (ML training servers) 
• Storage systems (NAS, object storage, distributed file systems) 

• Integrate hybrid networking: 

• VPN / Direct Connect / ExpressRoute 

• Enable workload portability across environments 

• Implement: 

• Endpoint management and security (e.g., Intune) 
• Backup and disaster recovery solutions 

7. Data Engineering & Pipelines

• Build scalable data pipelines for: 

• Streaming and batch workloads 

• Work with: 

• Kafka / PubSub / EventHub 
• Data lakes and warehouses 

• Ensure: 

• Data reliability 
• Observability 
• Governance 

8. Observability, CloudOps & FinOps

• Build centralized monitoring systems across multi-cloud and on-prem environments 

• Implement: 

• Metrics (Prometheus, cloud-native tools) 
• Logging (ELK / OpenSearch) 
• Tracing (OpenTelemetry / Jaeger) 

• Define and manage: 

• SLIs, SLOs, and alerting strategies 

• Apply CloudOps and FinOps principles for operational efficiency and cost control 

Required Skills & Experience

Experience & Ownership

• Total of 12 ~ 15 years of IT experience

• 8 ~ 10+ years in Cloud / Infra Platform Engineering / DevOps 

• Proven experience building and operating production-scale systems (0 → scale) 

• Strong ownership mindset: architecture + implementation + operations

Multi-Cloud & Core Infrastructure

• Hands-on experience with AWS, Azure, and GCP (minimum 2 at strong proficiency) 

• Deep understanding of: 

• Cloud architecture (IaaS, PaaS, SaaS) 
• Storage Services (AWS S3, Azure Blob storage, GCP Storage Bucket)
• Networking (VPC/VNet, routing, private connectivity) 
• IAM, security, and governance 

• Experience with: 

• High availability, multi-region design, and disaster recovery 
• Cloud cost optimization (FinOps awareness) 

Infrastructure as Code & Automation

• Advanced expertise in Terraform: 

• Modular design, remote state management, workspaces 
• CI/CD integration and environment promotion 

• Strong scripting skills (Python / Bash) 

Kubernetes & Distributed Systems

• Production experience with Kubernetes (AKS/EKS/GKE): 

• Cluster architecture, scaling, and operations 
• Networking, ingress, and service discovery 
• Multi-cluster or hybrid deployments 

• Strong understanding of distributed systems fundamentals 

DevSecOps

• Experience building secure CI/CD pipelines: 

• GitHub Actions / GitLab CI / Azure DevOps 

• Integration of: 

• SAST, DAST, container scanning, IaC scanning, VAPT 

• Experience with: 

• Secrets management (Vault, KMS) 
• Policy-as-code (OPA, Azure Policy, AWS SCPs) 
• Vulnerability management and patching 

MLOps & AI Infrastructure

• Hands-on experience with: 

• ML training pipelines and inference systems 

• Model deployment using: 

• Kubernetes / GPU clusters / serverless inference 

• Experience with: 

• Model lifecycle (versioning, CI/CD, monitoring) 
• GPU optimization and cost efficiency 

• Exposure to: 

• Azure AI / AWS SageMaker / GCP Vertex AI 

Hybrid Infrastructure (Cloud + On-Prem)

• Experience with: 

• On-prem compute and storage systems 
• Hybrid networking (VPN / Direct Connect / ExpressRoute) 

• Backup, disaster recovery, and resilience strategies 

Data Engineering & Pipelines

• Experience building: 

• Streaming and batch data pipelines 

• Familiarity with: 

• Kafka / PubSub / EventHub 
• Data lakes and warehouses 

• Understanding of data reliability and governance 

Observability & Reliability Engineering

• Hands-on with: 

• Prometheus, Grafana 
• ELK / OpenSearch 
• OpenTelemetry / Jaeger 

• Ability to define and implement: 

• SLIs / SLOs / alerting 

• Experience with centralized monitoring across hybrid environments 

Edge / Robotics / OTA (Preferred)

• Experience with: 

• OTA systems and edge deployments 

• Familiarity with: 

• ROS/ROS2 ecosystems 
• Containerized edge workloads 

Core Engineering Fundamentals (Non-Negotiable)

• Strong Linux fundamentals 

• Solid networking knowledge (L4–L7) 

• Ability to debug across layers (infra → network → application) 

Nice to Have

• Experience building Internal Developer Platforms (IDP) 

• Multi-cluster Kubernetes management across clouds 

• Experience with robotics simulation platforms (e.g., CARLA) 

• Exposure to endpoint management and security tools (e.g., Intune)