ML Engineer & MLOps Practitioner
Designing end-to-end ML pipelines, training deep learning models, and deploying scalable AI systems on cloud infrastructure. Self-supervised & contrastive learning specialist.
Current Stack
Python → Cloud
I'm a Machine Learning Engineer and MLOps practitioner pursuing my BE in Computer Science at SRMIST (Class of 2027). I specialise in self-supervised & contrastive representation learning, computer vision, anomaly detection, and time-series modelling — and I care deeply about making research actually reach production.
From building real-time intrusion detection systems to deploying cloud-native AI automation agents, I focus on reproducible experiments and inference pipelines that scale. I'm actively seeking roles where I can push label-efficient learning and satellite-based change detection for environmental impact.
From deep learning research to cloud deployment — a full-stack ML practitioner comfortable across the entire pipeline, from raw data to production inference.
Building at the intersection of ML research and production engineering, with a focus on model quality and RLHF pipelines.
Open to ML engineering roles, research collaborations, and consulting. Based in New Delhi — available remotely worldwide.