Professional Overview
AI Engineer with 2+ years of experience in Generative AI model evaluation, RLHF-based alignment, and multimodal dataset creation for large-scale LLM and vision-language systems at NVIDIA. Skilled in judging and improving model output quality across VQA, dense captioning, and embodied AI use cases, with a consistent track record
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- AI Engineer with 2+ years of experience in Generative AI model evaluation, RLHF-
Key Achievements / KPIs
- Created fully human-written, non-generic question–explanation–answer sets ground
- Dense Captioning: Wrote fine-grained, per-action captions for individual events
- RLHF & SFT: Compared pairs of model responses for quality, correctness, and help
- Human Egocentric & GR00T Egocentric: Captioned first-person (POV) video of human
AI Expertise
Skills
Employment Type
Description of Responsibilities
- AI Engineer with 2+ years of experience in Generative AI model evaluation, RLHF-
Key Achievements / KPIs
- Created fully human-written, non-generic question–explanation–answer sets ground
- Dense Captioning: Wrote fine-grained, per-action captions for individual events
- RLHF & SFT: Compared pairs of model responses for quality, correctness, and help
- Human Egocentric & GR00T Egocentric: Captioned first-person (POV) video of human
Education
Field of Study: CSE (Data Science)
Areas of Expertise
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
AI Product & Strategy
Translate AI capabilities into product roadmaps, define success metrics, and drive adoption across engineering and business teams.
