Computer Vision Engineer/ Lead

Infosys; Hyderabad, Pune District, Bengaluru; Hybrid; INR 1,200,000 - 2,400,000; Full time

Job summary

Infosys in India seeks a Computer Vision Engineer to design and deploy real-time image and video analytics solutions using deep learning, computer vision, and edge deployment.

You will develop and optimize models with PyTorch, TensorFlow, ONNX; implement multi-camera tracking, video analytics pipelines, and NVIDIA-based runtimes, ensuring scalable, production-ready systems.

Qualifications

• Hands-on experience in developing and deploying deep learning models for real-time image and video analytics.
• Strong background in computer vision, deep learning frameworks, and edge deployment technologies.
• Experience with multi-camera video analytics and object tracking is a plus.

Responsibilities

• Design, train, and optimize deep learning models for CV tasks such as detection, recognition, segmentation and video analytics.
• Lead large-scale real-time implementations involving 100+ video streams.
• Fine-tune CNNs and Vision Transformers using TensorFlow, PyTorch, and ONNX.
• Build and deploy inference pipelines using NVIDIA technologies and TAO/Triton.
• Apply pre- and post-processing to improve model performance and evaluation.
• Develop multi-object tracking across camera feeds and handle ROI extraction and action recognition.

Skills

Python

PyTorch

TensorFlow

Computer Vision

Edge deployment

MLOps

ONNX

Video analytics

Real-time processing

NVIDIA technologies

Tools

PyTorch

TensorFlow

ONNX

CUDA/NVIDIA tools

TAO Toolkit

Job description

We are seeking a highly skilled Computer Vision Engineer with hands-on experience in developing and deploying deep learning models for real-time image and video analytics. The ideal candidate will have a strong background in computer vision, deep learning frameworks, and edge deployment technologies.

Computer Vision Specialists with hands-on experience in:

• Python, PyTorch, TensorFlow
• CV/ML/DL model development
• Object detection & recognition, Action recognition
• NVIDIA technologies
• MLOps, Edge deployment

Roles & responsibilities-

• Design, train, and optimize deep learning models for computer vision tasks such as object detection, image recognition, segmentation, and video analytics.
• Lead large-scale real-time implementations involving 100+ video streams.
• Fine-tune models like CNNs and Vision Transformers using frameworks such as TensorFlow, PyTorch, and ONNX.
• Build and deploy inference pipelines using NVIDIA technologies including NGC models, TAO Toolkit, DeepStream Metropolis, and Triton Server.
• Apply pre- and post-processing techniques to enhance model performance and evaluation.
• Develop and implement multi-object tracking algorithms across multiple camera feeds., Perform camera calibration and extract regions of interest (ROI), action recognition using temporal analysis techniques.
• Develop models for feature extraction and come up with strategies for latent space analysis and manipulation of the data.
• Proficiency in writing complex SQL queries, familiarity with NoSQL or Graph Databases (Neo4j) is an advantage.
• Knowledge of infrastructure as code tools (e.g., Terraform, Ansible) is an advantage.
• Proficiency with containerization technologies (Docker) and orchestration tools (Kubernetes) is an advantage.