Own product quality across projects through manual and automated testing, test strategy, release-readiness decisions, and quality gates. Build automation frameworks using Pytest, Playwright, and Locust within CI/CD pipelines. Apply performance, security, chaos, API, and end-to-end testing, while collaborating with developers, machine learning engineers, designers, and AI stakeholders. Contribute to QA strategy, AI-assisted testing, continuous improvement, knowledge sharing, and junior QA onboarding.

Develop and productionize machine learning and generative AI systems. Responsibilities include building MLOps infrastructure, scalable training and deployment platforms, cloud-native APIs, inference pipelines, and agentic systems. The role optimizes models across hardware platforms, troubleshoots AI implementations, collaborates with clients and cross-functional teams, documents solutions, and mentors junior engineers.

As an AI/Machine Learning Engineer Intern, you will support deployment of AI models, develop scalable infrastructure, and write production-grade software. Work with senior engineers on generative AI projects and contribute to clear technical documentation.