Project Role : Cloud Platform Engineer
Project Role Description : Designs, builds, tests, and deploys cloud application solutions that integrate cloud and non-cloud infrastructure. Can deploy infrastructure and platform environments, creates a proof of architecture to test architecture viability, security and performance.
Must have skills : AWS Architecture
Good to have skills : NA
Minimum
7.5 year(s) of experience is required
Educational Qualification : 15 years full time education
Key Responsibilities:
- Design, develop, and maintain end-to-end cognitive HCIs, Private cloud integrating intelligence into traditional web stacks.
- Develop and manage full stack infrastructure Application including backend services (APIs, microservices) and API gateway for frontend and backend services.
- Understand the impact of GPU based computing and have experience in deploying High Performance Computing environments
- AWS Outpost, Azure Stack, Google cloud VPC Certified and implementation knowledge.
- Tanzu, Red Hat Openshift cluster deployment on Private cloud Deisgn, Deploy and Maintain.
- Develop cloud-native back-end services using Node.js, Python (FastAPI, Flask), or Java to connect AI models with application logic.
- Integrate AI/ML models (TensorFlow, PyTorch, scikit-learn) into production-ready APIs and microservices.
- Write efficient, maintainable code and manage integration between front-end interfaces and back-end infrastructure services.
- Collaborate with product, design, ML, and DevOps teams to build intelligent workflows and user experiences
- Implement Infrastructure as Code (IaC) using tools like Terraform, CloudFormation, AZURE DEV OPS or Pulumi.
- Deploy and manage Platform-as-a-Service (PaaS) offerings.
- Design, implement, and maintain database solutions, including relational databases (e.g., MySQL, PostgreSQL, SQL Server) and NoSQL databases (e.g., MongoDB, DynamoDB)
- Collaborate with DevOps, security, and development teams to ensure seamless integration and delivery.
- Ensure platform observability via metrics, logging, and monitoring frameworks (e.g., Prometheus, ELK, CloudWatch).
- Manage containerization and orchestration using Docker and Kubernetes.
- Ensure compliance with security best practices and organizational policies.
- Continuously evaluate and implement new cloud technologies and tools to improve efficiency.
- Provide technical guidance and support to team members and stakeholders.
- Integrate and support AI-driven tools and frameworks, including Generative AI and Agentic AI technologies, within cloud infrastructure and applications.
- Resource should be AI ready.
