Vision AI Solution Architect
Tata Consultancy Services · Pune · onsite · Posted 58d ago
Undisclosed8–12 yrsfulltimeSourced from
About a minute. You'll make an account on the way through.
About this role
- 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.
- Strong architectural and communication skills, with the ability to translate business needs into technical vision/GenAI solution designs.
Sourced from naukri
Skills
CLIPVLMGenerative Ai
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