Job Summary

We are looking for a technically robust Deep Learning Engineer with hands-on experience in Computer Vision and Vision-Language Models (VLMs) to build and deploy production-grade video analytics solutions.

The role involves owning computer vision solutions end-to-end — from model development and training to inference optimisation and deployment on live, multi-camera environments. The position requires strong Python and C++ skills and experience working with real-world camera streams.

Key Responsibilities
Build, train and evaluate computer vision models for detection, tracking, classification and segmentation.
Design and deploy end-to-end video analytics pipelines for live camera streams.
Develop and optimise multi-camera tracking across overlapping and non-overlapping camera views.
Apply Vision Language Models (VLMs) for real-world scene understanding, including prompting, grounding, evaluation and pipeline integration.
Optimise deep learning models for real-time inference on GPU and CPU infrastructure.
Build and automate data pipelines covering ingestion, annotation, augmentation and dataset versioning.
Debug and improve model performance on challenging real-world footage, including poor lighting, occlusion, camera drift and varying camera angles.
Deploy,



monitor and continuously retrain models in production.

Requirements

Key Requirements
Robust hands-on experience in Deep Learning and Computer Vision.
Proven experience deploying computer vision models in production environments.
Experience working with live camera/video streams.
Strong programming skills in Python and C++.
Experience with object detection, tracking, classification and segmentation.
Experience building end-to-end video analytics pipelines.
Hands-on exposure to multi-camera tracking.
Experience with Vision-Language Models (VLMs) and practical applications of prompting, grounding and evaluation.
Experience optimising models for real-time inference on GPU/CPU.
Solid understanding of model training, evaluation, deployment and monitoring.
Ability to troubleshoot models against real-world video quality challenges.

Preferred Skills
PyTorch / TensorFlow
OpenCV
CUDA / TensorRT
ONNX / model optimisation
Object detection and tracking frameworks
Multi-camera / Re-ID systems
VLM / LLM integration
GPU inference optimisation
Docker and production deployment
Video streaming technologies

📌 Computer Vision / Vlm Bengaluru
🏢 Kloudspot
📍 Bengaluru