• Supporting ai-driven client engagements across automation, data extraction, and intelligence workflows for enterprise use cases.
• Assisting in the development of ai-enabled solutions for client-facing problem statements, including research, workflow design, technical feasibility analysis, and prototype refinement.
• Collaborating with internal teams to translate business requirements into practical ai and ml implementation plans with a focus on reliability, scalability, and delivery readiness.

• Completed an intensive research-grade curriculum covering supervised learning, deep neural networks, probabilistic graphical models, generative ai, and reinforcement learning.
• Gained hands-on exposure to production-scale ml systems and model training and evaluation practices shared by senior amazon scientists and principal engineers.

• Conducted first-principles device simulation of lead-free perovskite solar cells using scaps-1d, varying absorber thickness, layer combinations, and back-contact work function to optimize efficiency.
• Co-authored and presented the paper 'enhancing efficiency of lead-free perovskite solar cell by varying thickness, layer combination and back contact work function' at ieee icecct 2024.

led 11 department heads and more than 100 executives across technical programming, workshops, and digital outreach for the student community.