Computer Vision Scientist

Computer Vision Scientist - AI Labs

Responsibilities

Computer Vision Scientist-AI Labs

AI Model Development & Deployment:

Design, train, and optimize deep learning-based computer vision models, including Object Detection (OD), Optical Character Recognition (OCR), 3D vision, Event Detection (ED), Satellite Image Processing (SI), and Thermal Imaging (TI).

Develop custom deep learning architectures using CNNs, transformers, GANs, and YOLO-based frameworks for real-time applications.

Implement and fine-tune RGB-D image processing algorithms for depth estimation, multi-view stereo, and LiDAR-based vision.

Optimize AI inference speed, accuracy, and computational efficiency to support real-time processing at the edge and cloud environments.

Data Engineering & Feature Extraction:

Preprocess large-scale image video datasets, performing data augmentation, feature extraction, and dimensionality reduction.

Develop custom image segmentation, object tracking, and feature detection pipelines.

Implement efficient data pipelines for structured and unstructured image processing, integrating with HPC clusters and cloud-based storage.

AI Training & Model Optimization:

Train state-of-the-art neural networks (CNNs, RNNs, ViTs, RAG, GANs, Transformers, etc.) on complex datasets.

Implement MLOps workflows, leveraging TensorFlow, PyTorch, OpenCV, and Hugging Face for automated model training.

Ensure model generalization by experimenting with data augmentation, fine-tuning, and adversarial training techniques.

Image Analytics & Real-Time AI Deployment:

Deploy AI models on cloud (AWS, GCP, Azure) and edge devices (Jetson Nano, TensorRT, OpenVINO).

Integrate computer vision models with IoT cameras, satellite feeds, and industrial automation systems.

Develop real-time analytics dashboards to visualize AI model insights for business stakeholders.

Cross-Functional Collaboration & Execution:

Work closely with AI engineers, data scientists, and software teams to integrate CV models into enterprise applications.

Support R&D teams in testing and refining AI-powered robotics, drone vision, and autonomous navigation solutions.

Collaborate with business teams to align AI-driven insights with operational goals and decision-making.

AI Governance, Compliance & Risk Management:

Ensure model fairness, bias mitigation, and explainability for AI-driven computer vision applications.

Comply with data privacy regulations (GDPR, AI Act) and ethical AI standards for responsible AI development.

Monitor and address AI model drift, hallucinations, and security vulnerabilities.

Key Stakeholders - Internal

Computer Vision Head

Data Science & Engineering Teams

Business & Product Teams

Cloud & DevOps Teams

Key Stakeholders - External

AI Research Institutions & Tech Partners

Regulatory Bodies

Cloud & AI Infrastructure Providers

Qualifications

Educational Qualification:

Master’s Ph.D. in AI, Computer Vision, Machine Learning, or Computer Science (Preferred from ISI IISc IIT Top AI Institutes)

Certification:

(Preferred but Not Mandatory)

AWS GCP Azure AI & Machine Learning Certification

Deep Learning Specialization (Coursera, Stanford, Andrew Ng’s DL Program)

Computer Vision Nanodegree (Udacity, MIT AI)

DevOps for AI & MLOps Certification (Google, Kubernetes, TensorFlow Extended – TFX)

Work Experience (Range of years):

5-10 yrs