Key Responsibilities :

- Lead the design and development of computer vision and machine learning solutions across object detection, image segmentation, classification, pose estimation, OCR, and tracking.

- Evaluate and fine-tune pre-trained architectures such as ResNet, YOLO, ViT, DETR, SAM, and CLIP based on data availability and business use cases.

- Design, develop, and optimize deep learning models using PyTorch and TensorFlow.

- Leverage computer vision libraries and frameworks including OpenCV, Detectron2, MMDetection, Hugging Face Transformers, and timm.

- Design or adapt CNNs, Vision Transformers, GANs, diffusion models, and other architectures for specific business and technical requirements.

- Apply transfer learning and parameter-efficient fine-tuning techniques such as LoRA and adapters to efficiently adapt large and foundation models.

- Build end-to-end data pipelines covering data collection, annotation, augmentation, and synthetic data generation using tools such as CVAT and Labelbox.

- Optimize models for production using quantization, pruning, and knowledge distillation.

- Deploy models using TensorRT, ONNX, OpenVINO, and edge-device platforms.

- Establish and implement MLOps practices including experiment tracking, model versioning, ML CI/CD, and production monitoring using tools such as MLflow and Weights & Biases.

- Work with cloud platforms such as AWS, GCP, and Azure and containerization technologies including Docker and Kubernetes.

- Explore and implement sensor-fusion and multimodal solutions involving LiDAR, depth cameras, and other sensor data where applicable.

- Lead technical reviews, establish engineering best practices, and ensure high-quality, maintainable solutions.

Leadership & Soft Skills :

- Proven experience leading and mentoring teams of Computer Vision / ML Engineers.

- Strong experience conducting code reviews and driving technical growth within the team.

- Ability to evaluate build-vs-fine-tune-vs-buy trade-offs and define realistic technical roadmaps.

- Proven track record of taking ML/CV models from prototype to production at scale.

- Strong cross-functional collaboration with Product, Data, and Engineering teams.

- Ability to stay current with computer vision research, emerging foundation models, and industry developments.

- Strong communication skills with the ability to explain complex technical concepts and trade-offs to technical and non-technical stakeholders.

Preferred Skills :

- Experience with LiDAR, depth cameras, or multimodal/sensor-fusion applications.

- Experience deploying computer vision models on edge devices.

- Strong understanding of emerging vision foundation models and practical approaches to model adaptation and deployment.