Lead AI Engineer
๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฏ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ณ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฏ๐ฌ-๐ณ๐ฌ ๐๐ฃ๐)
Experience: 8+ yrs
Location: Bengaluru
Job Type: Full-time
We are looking for a highly experienced AI Research Engineer to join an advanced AI research team focused on transforming cutting-edge developments in Vision-Language Models (VLMs), Vision-Language Action Models (VLAs), diffusion models, and multimodal AI into robust, real-time systems for dynamic construction environments.
This role offers an opportunity to work at the intersection of deep learning research, computer vision, generative AI, robotics, and edge deployment. You will be responsible for taking research concepts from problem definition and experimentation through evaluation and production hand-off. The ideal candidate will have strong research depth, hands-on experience with large-scale AI systems, and the ability to translate complex mathematical and theoretical concepts into reliable production solutions.
Requirements
Key Responsibilities
- Research and develop diffusion-based generative models for photorealistic surface simulation, defect synthesis, and domain adaptation.
- Design and train VLM and VLA architectures that integrate textual instructions, CAD plans, visual inputs, and sensor data.
- Develop scalable auto-annotation and data-centric AI pipelines using active learning, pseudo-labeling, self-training, weak supervision, and synthetic data.
- Build and optimize deep-learning models for large-scale training and real-time inference.
- Apply techniques such as INT8 quantization, LoRA, knowledge distillation, and model compression for edge deployment.
- Optimize models for Jetson-class hardware, CUDA, TensorRT, and ONNX Runtime within robotics environments.
- Own the complete research lifecycle, including problem definition, literature review, prototyping, experimentation, evaluation, and production hand-off.
- Establish offline and online evaluation frameworks to measure model accuracy, robustness, latency, and scalability.
- Collaborate with perception, robotics, controls, and engineering teams to integrate AI solutions into production systems.
- Prepare internal technical reports and contribute to external research publications and conferences.
- Mentor interns and junior AI/ML engineers and provide technical leadership on research initiatives.
What's Makes You a Great Fit
- 8+ years of experience in deep-learning research and development, or an advanced degree such as an M.S./Ph.D. in Computer Science, Electrical Engineering, Robotics, or a related discipline.
- Strong hands-on expertise in diffusion models, including DDPM, LDM, and ControlNet.
- Experience with multimodal transformers and Vision-Language Models, such as CLIP, BLIP-2, LLaVA, or Flamingo.
- Proven experience building large-scale, data-centric AI workflows involving active learning, pseudo-labeling, weak supervision, or synthetic data.
- Advanced proficiency in Python and PyTorch or JAX, along with experience in scalable model training and experiment tracking.
- Familiarity with PyTorch Lightning, DeepSpeed, Ray, or comparable distributed-training frameworks.
- Strong understanding of CUDA, C++, TensorRT, and ONNX Runtime for performance optimization and edge AI deployment.
- Solid mathematical foundation in probability, optimization, information theory, and machine learning.
- Ability to translate advanced research concepts into clean, scalable, production-ready implementations.
- Strong problem-solving, research, communication, and technical leadership skills.
- Experience with ROS 2, Nav2, MoveIt 2, Open3D, or robotics perception systems is an added advantage.
- Exposure to synthetic data generation using platforms such as Isaac Sim will be highly valuable.