AI / ML Solutions Architecture - Computer Vision

Requirements:
Strong foundation in computer vision techniques: object detection, image segmentation, classification, pose estimation, OCR, and tracking.
Experience in fine-tuning pre-trained architectures (ResNet, YOLO, ViT, DETR, SAM, CLIP), choosing the right approach based on data availability and use case.
Proficiency in deep learning frameworks PyTorch and TensorFlow, along with CV libraries like OpenCV, Detectron2 MMDetection, and Hugging Face Transformers/timm.
Solid understanding of neural network architectures for vision (CNNs, Vision Transformers, GANs, and diffusion models) and ability to design or adapt them for specific problems.
Experience with transfer learning and parameter-efficient fine-tuning (LoRA, adapters) to adapt large/foundation models efficiently.
Skilled in data pipeline development: collection, annotation (CVAT, Labelbox), augmentation, and synthetic data generation.
Experience with model optimization for production: quantization, pruning, distillation, and deployment via TensorRT, ONNX, OpenVINO, or on edge devices.
Working knowledge of MLOps practices: experiment tracking (MLflow, W& B), CI/CD for ML, model versioning, and production monitoring.
Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) for scalable deployment.
Sensor fusion/multi-modal data experience (LiDAR, depth cameras) a plus, depending on the domain.
Leadership and Soft Skills:
Proven experience leading a team of CV/ML engineers, mentoring, code reviews, and technical growth.
Ability to evaluate build-vs-fine-tune-vs-buy trade-offs and set realistic project roadmaps.
Track record of shipping models from prototype to production at scale.
Strong cross-functional collaboration with product, data, and engineering teams.
Stays current with CV research and emerging pre-trained models, applying them pragmatically.
Clear communicator of technical trade-offs to both technical and non-technical stakeholders.

Experience
9-13 yrs