Lead AI Engineer

**This role is for one of the Weekday's clients** Min Experience: 8+ years Location: Bengaluru JobType: full-time **Key Responsibilities** - Research & innovate diffusion-based generative models for photorealistic wall-surface simulation, defect synthesis and domain adaptation. - Architect and train Vision-Language Models (VLMs) and Vision-Language Action Models (VLA) objectives that connect textual work orders, CAD plans and sensor data to pixel-level understanding. - Lead development of auto-annotation pipelines (active learning, self-training, synthetic data) that scale to millions of frames and point-clouds with minimal human effort. - Optimize and compress models (INT8, LoRA, distillation) for deployment on Jetson-class edge devices under ROS 2. - Own the full lifecycle—problem definition, literature review, prototyping, offline/online evaluation and production hand-off to perception & controls teams. - Publish internal tech reports and external conference papers; mentor interns and junior engineers. ### **Requirements** **Qualifications & Skills** - 8+ years in deep-learning R&D or Ph.D./M.S. in CS, EE, Robotics or related field with strong publication record. - Demonstrated expertise in diffusion models (DDPM, LDM, ControlNet) and multimodal transformers / VLMs (CLIP, BLIP-2, LLaVA, Flamingo). - Proven success building large-scale data-centric AI workflows—active learning, pseudo-labeling, weak supervision. - Advanced proficiency in Python, PyTorch (or JAX), experiment tracking and scalable training (PyTorch Lightning, DeepSpeed, Ray). - Familiarity with edge-AI runtimes (TensorRT, ONNX Runtime), and CUDA / C++ performance tuning. - Strong mathematical foundation (probability, information theory, optimization) and ability to translate theory into production code. - Bonus: experience with synthetic data generation in Isaac Sim or robotics perception stacks (ROS2, Nav2, MoveIt 2, Open3D). ### Must-have skills AI, Diffusion Models, Python ### Good-to-have skills DDPM, CLIP, Pytorch