Senior Applied ML Engineer - Computer Vision in Bangalore, भारत

Role : Senior Applied ML https://jobeax.com/link/S0veizAgyg6cBkoP Overview : You will build computer-vision and multimodal systems that understand real-world video. You will own the full model lifecycle : problem definition, data strategy, experimentation, evaluation, production launch, and post-launch improvement. Success means reliable production behavior - not benchmark performance or impressive demos https://jobeax.com/link/gOXwWQpdcG57e1H2 You'll Work On : - Face, person, object, and sensitive-text detection.- Object and person tracking.- Temporal event and action recognition.- Video quality assessment.- Evidence extraction and video summarization.- Multimodal video https://jobeax.com/link/RSrMpCD1qvAQtvQI You'll Do : - Build representative training and evaluation datasets from field footage.- Design annotation guidelines, sampling strategies, hard-negative mining, and active-learning workflows.- Define model metrics connected to product outcomes : privacy-critical false negatives, precision and recall, confidence calibration, human-review burden, and performance across operating conditions.- Establish strong baselines, experiment tracking, model cards, and launch criteria.- Diagnose failures frame by frame and convert patterns into data, modeling, or product improvements.- Use production failures and reviewer feedback to improve datasets and models.- Work with the ML Evaluation engineer to define quality standards and regression tests.- Work with backend and platform engineers to package, deploy, monitor, and roll back models.- Communicate model limitations, uncertainty, and trade-offs https://jobeax.com/link/yRu5kGQbmF5N5FhC We're Looking For : - Strong Python and PyTorch experience.- Experience shipping computer-vision models into production.- Strong foundation in several areas : object detection and segmentation, OCR, multi-object tracking, action recognition, temporal localization, and video or vision-language models.- Strong understanding of precision/recall trade-offs, calibration and threshold selection, dataset leakage, label quality, distribution shift, and stratified evaluation.- Experience building datasets and evaluation systems for messy real-world inputs.- Ability to independently own ambiguous ML problems from framing through production.- Strong software-engineering fundamentals.- Ability to explain complex model behavior to product and operations teams. (ref:hirist.tech)