Role & responsibilities

- Design and maintain scalable cloud infrastructure, platform services, and Infrastructure-as-Code (IaC) solutions.
- Implement and support AI Quality Automation platforms, CI/CD pipelines, and cloud-based engineering solutions. • Design and maintain scalable cloud infrastructure, platform services, and Infrastructure-as-Code (IaC) solutions
- Build and manage automated deployment, monitoring, observability, and reliability frameworks.
- Develop and support containerization and orchestration solutions using technologies such as Docker and Kubernetes.
- Establish LLMOps, DevOps, and DevSecOps practices supporting AI model and workflow lifecycle management.
- Support integrations across Dify, AIQ, GitLab, Jira, Zephyr, Confluence, Selenium/Fusion, and enterprise engineering tools.
- Develop automation utilities, APIs, deployment pipelines, and platform services to improve engineering productivity.
- Monitor platform health, reliability, performance, security, and operational readiness.
- Partner with Cloud, Infrastructure, Security, Product, and SQA teams to ensure platform stability and compliance.
- Create operational documentation, runbooks, support procedures, and implementation standards.

Preferred candidate profile

- Enterprise AI Automation Strategy, Vision, and Roadmap
- AI Automation Reference Architectures, Standards, and Governance Frameworks
- Intelligent Automation Frameworks, Reusable Components, and Best Practices
- AI-Powered Test Automation Frameworks, Autonomous Testing Solutions,



and Self-Healing Automation Models
- Automation and AI Adoption Plans aligned with business and technology objectives
- Proofs of Concept (POCs), Proofs of Technology (POTs), Pilot Programs, Prototypes, and Research Findings
- Technology Assessments, Tool Evaluations, Vendor Comparisons, and Recommendations for emerging AI and automation platforms (e.g., Cypress, Playwright, Selenium, APPVANCE AIQ, Agentic AI solutions)
- CI/CD, DevOps, and Platform Automation Design Artifacts
- AI-Driven Quality Engineering Strategies, Test Generation Models, and Predictive Quality Analytics
- Automation Asset Repositories including reusable scripts, workflows, test data, AI agents, libraries, and templates
- Automation Governance Documentation, Policies, Guidelines, and Responsible AI Standards
- Automation Maturity Assessments and Continuous Improvement Roadmaps
- Training Materials, Knowledge Transfer Artifacts, and Automation Playbooks
- Process, Application, and Technology Modernization Recommendations to improve efficiency, quality, security, scalability, and business outcomes.

Expected Business Outcomes

- Increased automation adoption and reuse across IT organization
- Reduced delivery and testing cycle times
- Improved software quality, reliability, and compliance
- Increased operational efficiency and productivity
- Accelerated adoption of AI-enabled engineering practices
- Measurable cost savings, risk reduction, and business value realization

📌 Staff AI Agentic Automation Platform Engineer (Noida)
🏢 ETS Assessment Services
📍 Noida