Top Tech Jobs & Startup Jobs in Chennai

Develop and maintain non-UI test automation frameworks and automated tests (using Playwright and internal tools), execute manual tests, design test plans, write test documentation, use AI tools (MCPs, Copilot, Claude) and build AI agents to automate workflows, review product code and engineering documents, troubleshoot software issues, and resolve customer-reported problems.

Design and build test automation strategies and frameworks using Core Java; develop and execute automated functional and non-functional test scenarios; analyze manual QA for automation opportunities; debug and isolate defects; evaluate new tools; work in Agile with familiarity of CI/CD pipelines.

Design, develop, and debug complex software for semiconductor equipment. Create software specifications, test plans, documentation, and test procedures. Troubleshoot and fix difficult software issues, implement fixes, and collaborate with internal and external customers on requirements and schedules.

Create and maintain high-quality software documentation (online help, release notes, installation instructions). Collaborate with engineers, product managers, QA, and support to gather information, use software and VMs to explore features, produce diagrams/screenshots, and deliver accurate documentation on schedule while using AI tools to improve productivity.

The Senior Backup/Storage Engineer is responsible for managing and supporting backup systems using tools like ECS and Networker, ensuring data recovery capabilities, and handling upgrades and configurations for storage environments.

Lead a backend/product software team (7-10 developers) to deliver end-to-end Java-based features and fixes. Provide technical mentorship, contribute hands-on coding and architecture (HLD/LLD), ensure software quality, CI releases, and work with large data sets and databases. Interface with stakeholders, operate in a multi-site Agile environment, and split time between technical delivery and management.

Research and develop domain-specific LLMs and generative models for materials science: pretraining, fine-tuning, alignment (e.g., RLHF), evaluation, dataset curation, and benchmark creation. Collaborate with scientists and engineers to apply generative AI to discovery and hardware design, publish research, and mentor junior team members.

Design and implement scalable high-performance computing infrastructures for chip manufacturing, focusing on GPU and CPU integration, parallel programming, and optimization.