Job description
Project Role : Operations Engineer
Project Role Description : Support the operations and/or manage delivery for production systems and services based on operational requirements and service agreement.
Must have skills : Cloud Infrastructure
Good to have skills : NA
Minimum 3 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary The Cognitive PaaS Full Stack Engineer designs and develops cognitive cloud applications and services ranging from user interfaces to API layers and AI integration middleware. The engineer works across front-end, back-end, and cloud infrastructure layers to deliver intelligent, data-driven solutions that leverage AI/ML models, agentic workflows, and cognitive automation frameworks. Key Responsibilities: Design, build, and maintain scalable, secure, and highly available cloud infrastructure on platforms such as AWS, Azure, or Google Cloud. Design, develop, and maintain scalable frontend and backend applications that automate and manage IaaS resources, such as compute, network, and storage.
Develop and manage full stack infrastructure Application including backend services (APIs, microservices) and API gateway for frontend and backend services.
Knowledge of Application Architecture Microservices vs Event Driven Architecture Vs Cloud Based Architecture Vs Cloud native architecture
Good experience in application modernization support, deploy, manage and maintain cloud native applications on Paas/ Cloud Native Architecture
Write efficient, maintainable code and manage integration between front-end interfaces and back-end infrastructure services.
Implement Infrastructure as Code (IaC) using tools like Terraform, CloudFormation, or Pulumi.
Automate deployment pipelines and manage CI/CD workflows using tools such as Jenkins, GitLab CI, or GitHub Actions.
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) Monitor, troubleshoot, and optimize cloud infrastructure and application performance.
Collaborate with DevOps, security, and development teams to ensure seamless integration and delivery. 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. Cloud Security knowledge and implementation experience
Cloud Landing zone network requirements knowledge and experience in implementing the right level of network capability for the cloud landing zones. Basic knowledge and experience in Networking for cloud landing zones
Required Skills: Strong proficiency in cloud platforms - AWS, Azure, and Google Cloud.
Hands-on experience with Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Pulumi. Good programming skills across both frontend (HTML, CSS, JavaScript, React/Angular) and backend (Python, Node.js, Java, etc.) stacks. Expertise in containerization and orchestration (Docker, Kubernetes).
Experience with Platform-as-a-Service (PaaS) offerings like AWS Elastic Beanstalk, Azure App Services, or Google App Engine. Strong database skills with experience designing and managing relational (MySQL, PostgreSQL, SQL Server) and NoSQL (MongoDB, DynamoDB) databases. Familiarity with CI/CD pipelines and automation tools (Jenkins, GitLab CI, GitHub Actions, & Ansible). Strong understanding of networking, security, and cloud architecture best practices. Ability to troubleshoot complex infrastructure and application issues. Excellent communication and collaboration skills. Experience with monitoring tools such as Prometheus, Grafana, ELK stack, or Cloud-native monitoring solutions. Knowledge and hands-on experience with Generative AI (e.g., large language models, text/image generation) and Agentic AI frameworks Proficiency with JIRA for issue tracking and project management. Experience working in Agile environments using Kanban methodologies
Certifications (Required / Preferred): AWS Certified Solutions Architect Professional Microsoft Certified: Azure Solutions Architect Expert Google Professional Cloud Architect Certified DevOps Engineer certifications (AWS, Azure, or Google) Resource needs to be AI Ready.
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