We are looking for highly skilled Computer Vision Architects / Engineers with strong expertise in deep learning, real-time video analytics, and edge AI deployments. The ideal candidate will have hands-on experience in building, optimizing, and deploying computer vision and AI/ML models at scale, particularly for image and video-based use cases using Python, PyTorch, TensorFlow, and NVIDIA platforms.
This role involves working on large-scale, real-time systems (100+ video streams) and contributing to end‑to‑end AI pipelines—from model development to edge & cloud deployment.
Key Responsibilities-
- Design, train, optimize, and deploy computer vision models for:
- Object detection & recognition
- Image classification & segmentation
- Action recognition and video analytics
- Lead and contribute to large-scale real-time deployments handling 100+ concurrent video streams.
- Fine-tune and optimize CNNs, Vision Transformers (ViT) and other deep learning architectures.
- Build scalable inference pipelines using PyTorch, TensorFlow, ONNX.
- Deploy and optimize models using NVIDIA AI stack, including:
- NVIDIA TAO Toolkit
- DeepStream SDK (Metropolis)
- Triton Inference Server
- NGC Pre-trained Models
- Implement advanced multi-object tracking (MOT) across multi-camera systems.
- Perform camera calibration, ROI extraction, and spatial-temporal analysis.
- Develop feature extraction models, latent space analysis strategies, and representational learning techniques.
- Apply robust pre-processing, post-processing, and evaluation techniques to improve model accuracy and performance.
- Collaborate with cross-functional teams on MLOps, CI/CD pipelines, and edge deployment strategies.
Required Skills & Qualifications-
- Strong proficiency in Python
- Hands-on experience with:
- PyTorch, TensorFlow
- Computer Vision, Machine Learning, Deep Learning
- Expertise in:
- Object Detection, Object Recognition
- Action Recognition & Video Analytics
- Multi-object tracking
- Experience with NVIDIA GPU-based acceleration and AI frameworks
- Practical exposure to edge AI deployment & inference optimization
- Experience in real-time video processing pipelines
Good to Have / Preferred Skills-
- Experience with MLOps frameworks and AI model lifecycle management
- Strong database knowledge:
- Writing complex SQL queries
- Familiarity with NoSQL / Graph Databases (Neo4j)
- Infrastructure as Code tools:
- Terraform, Ansible
- Containerization & orchestration:
- Docker, Kubernetes
- Experience deploying AI workloads on embedded / edge devices
Location-
- Pune, Bangalore, Chennai, Hyderabad, Trivandrunm, Noida, Kolkata
