Design, develop, and deploy scalable and agentic AI solutions for enterprise use cases across domains like finance, sales, marketing, and retail, focusing on innovation and utility, with a user-centric perspective.

Take ownership of AI quality for production systems by defining technical metrics aligned with business goals, implementing evaluation frameworks, designing experiments, analyzing loss patterns, and driving improvements through system changes or training data enhancements.

Implement, optimize, and advance AI techniques, with both training-free and training-based approaches.

Drive progress through experimentation cycles such as proposing hypotheses, designing validation methods, implementing and testing ideas, analyzing results, and iterating quickly to find optimal solutions.

Provide technical leadership on projects and facilitate clarity on goals and timelines. Work with a small, experienced team of developers, researchers, engaging in design and code reviews to foster a culture of continuous improvement.

PhD degree in Computer Science, a related field, or equivalent practical experience.

4 years of experience with research agendas across multiple teams or projects.

2 years of experience with machine learning algorithms and tools, or Applied ML (e.g., LLM's, Generative AI, NLP, Recommendations).

Experience coding in Python, JavaScript, R, Java, or C++.

One or more scientific publication submissions for conferences, journals, or public repositories (e.g., CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).