- Strong hands-on experience with CLIP and other contrastive embedding models, and Vision-Language Models (VLMs) for tasks such as captioning, visual QA, and grounding.
- Practical experience designing Vision RAG systems, including multimodal embeddings, vector databases, and retrieval-augmented generation patterns.
- Experience fine-tuning and adapting vision/VLM models using parameter-efficient techniques (LoRA/QLoRA) and classical CV model training/transfer learning.
- Working knowledge of hyperscaler AI/vision services across at least two of AWS, GCP, and Azure, and their trade-offs for vision workloads.
- Familiarity with edge AI deployment hardware accelerators (NVIDIA Jetson, Coral/edge TPU), model compression, quantization, and runtime optimization.
- Proficiency in Python and vision/ML frameworks (PyTorch, Hugging Face Transformers, OpenCV) and model format/runtime tooling (ONNX, TensorRT, OpenVINO).
- Understanding of containerization and orchestration (Docker, Kubernetes) for scalable vision model serving.
- Solid architectural and communication skills, with the ability to translate business needs into technical vision/GenAI solution designs.

📌 Vision AI Solution Architect (Bengaluru)
🏢 Tata Consultancy Services
📍 Bengaluru