Skills
What I actually use at work — with context on how and why.
Modeling & ML
My modeling work spans supervised ranking, classification, and generative tasks. At Myntra I work primarily with gradient-boosted trees and neural ranking models for CTR prediction on sparse ad auction data. At MIDAS Lab I fine-tune and evaluate vision-language models for annotation tasks and small LLM generation improvement.
Data Engineering
I build feature pipelines that are idempotent, testable, and reproducible. On the AdsRank team I re-architected snapshot-based pipelines on Databricks and PySpark, replacing ad-hoc scripts with versioned feature tables. I care about lineage and correctness first, throughput second.
Production Infrastructure
I've deployed inference APIs using FastAPI and Celery for async batch workloads, containerized with Docker, and monitored with Grafana/Prometheus. I'm comfortable with the DevOps side of ML — experiment tracking, model registries, and CI pipelines for data and model validation.
LLMs & Multimodal AI
I work on LLM evaluation, prompting strategies, and lightweight fine-tuning. My research at MIDAS Lab focuses on improving small LLM generation quality with Mixture of Refinement Agents. I've also built multimodal pipelines using vision-language models (QWEN-2.5-VL, CLIP) for classification and anomaly detection tasks.