Lead AI Engineer
About this role
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 experiencedย AI Research Engineerย to join an advanced AI research team focused on transforming cutting-edge developments inย Vision-Language Models (VLMs), Vision-Language Action Models (VLAs), diffusion models, and multimodal AIย into robust, real-time systems for dynamic construction environments.
This role offers an opportunity to work at the intersection ofย 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.
Key Responsibilities
- Research and developย diffusion-based generative modelsย for photorealistic surface simulation, defect synthesis, and domain adaptation.
- Design and trainย VLM and VLA architecturesย that integrate textual instructions, CAD plans, visual inputs, and sensor data.
- Develop scalableย auto-annotation and data-centric AI pipelinesย 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 asย INT8 quantization, LoRA, knowledge distillation, and model compressionย for edge deployment.
- Optimize models forย Jetson-class hardware, CUDA, TensorRT, and ONNX Runtimeย 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 experienceย 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 inย diffusion models, including DDPM, LDM, and ControlNet.
- Experience withย multimodal transformers and Vision-Language Models, such as CLIP, BLIP-2, LLaVA, or Flamingo.
- Proven experience building large-scale, data-centric AI workflows involvingย active learning, pseudo-labeling, weak supervision, or synthetic data.
- Advanced proficiency inย Python and PyTorch or JAX, along with experience in scalable model training and experiment tracking.
- Familiarity withย PyTorch Lightning, DeepSpeed, Ray, or comparable distributed-training frameworks.
- Strong understanding ofย CUDA, C++, TensorRT, and ONNX Runtimeย for performance optimization and edge AI deployment.
- Solid mathematical foundation inย 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 withย ROS 2, Nav2, MoveIt 2, Open3D, or robotics perception systemsย is an added advantage.
- Exposure to synthetic data generation using platforms such asย Isaac Simย will be highly valuable.
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