Senior Applied ML Engineer

Role - Senior Applied ML Engineer

Location - Bangalore, Karnataka

Experience - 6-10 yrs

Role 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 alone.

Must Have - Build Computer-Vision and Multimodal systems

What 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 understanding

What 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 clearly

What 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