Back
Metro Bangalore and popular ML title increase competition, but niche diffusion/edge/robotics skills limit applicant pool.
Specialized diffusion, VLM, and edge-robotics expertise limits cross-industry transferability.
Explicit 8+ years, PhD/MS, publication record and specialized tech mandates make filters highly strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop and deploy advanced vision-language and diffusion AI models for real-time perception and action on dynamic construction sites.
Lead end-to-end AI system development including research, prototyping, evaluation, optimization, and production hand-off to cross-functional teams.
Drive scaling of data annotation pipelines and model optimization for edge device deployment under ROS 2 environments.
8+ years of deep learning R&D experience or Ph.D./M.S. in CS, EE, Robotics, or related field with strong publication record.
Expertise in diffusion models (DDPM, LDM, ControlNet) and multimodal transformers/VLMs (CLIP, BLIP-2, LLaVA, Flamingo).
Proficient in Python, PyTorch (or JAX), scalable training frameworks (PyTorch Lightning, DeepSpeed, Ray), and AI model deployment on edge devices (TensorRT, ONNX Runtime).
Work Experience Required: 8+ years in AI/Deep learning R&D, Ph.D./M.S. in relevant technical domains.
Highly experienced in research-driven development of large-scale vision-language AI models and autonomous systems.
Comfortable leading technical ownership across AI research, data pipeline automation, model optimization, and production transition phases.
Practical knowledge of edge AI deployment constraints, ROS2-based robotics stacks, and data-centric AI workflows with active learning and synthetic data generation.