Lead AI Engineer

Ovii's Interpretation of the Role

Lead AI Engineer driving research and production of vision‑language and diffusion models for autonomous construction robots. Own end‑to‑end AI lifecycle, mentor junior talent, and publish internal and external findings.

Role Snapshot

• AI research leadership
• Vision‑language model development
• Diffusion‑based generative modeling
• Edge‑AI deployment on robotics platforms
• Large‑scale data pipeline engineering
• Mentorship of interns and junior engineers

Must-Have Requirements

• 8+ years deep‑learning R&D
• Diffusion model expertise (DDPM, LDM, ControlNet)
• Multimodal transformer / VLM experience (CLIP, BLIP‑2, LLaVA, Flamingo)
• Large‑scale data‑centric AI workflow experience
• Advanced Python proficiency
• Proficiency in PyTorch or JAX
• Experience with experiment tracking and scalable training (PyTorch Lightning, DeepSpeed, Ray)
• Familiarity with edge‑AI runtimes (TensorRT, ONNX Runtime)
• CUDA / C++ performance tuning
• Strong mathematical foundation (probability, information theory, optimization)
• Strong publication record
• Ph.D. or M.S. in CS, EE, Robotics or related field
• deep‑learning research
• diffusion model development
• multimodal transformer work
• large‑scale AI data pipelines
• edge‑AI deployment
• Ph.D. in Computer Science, Electrical Engineering, Robotics or related field
• M.S. in Computer Science, Electrical Engineering, Robotics or related field

Nice-to-Have Signals

• Synthetic data generation in Isaac Sim
• Robotics perception stacks (ROS2, Nav2, MoveIt2, Open3D)
• synthetic data generation for robotics
• ROS‑based perception stacks

Work Setup

• Location: Bengaluru, India
• Work mode: ONSITE
• Employment type: Full-Time

Eligibility Gates

• Visa sponsorship: unknown

Not Specified in JD

• Visa sponsorship
• Salary range
• Remote eligibility
• Education requirement specifics beyond degree
• Certifications
• Relocation
• Notice period
• Travel
• Security clearance
• Coding test
• Portfolio
• GitHub
• Writing sample
• Cover letter

What You'll Likely Work On

• Research and create diffusion‑based generative models for photorealistic wall‑surface simulation and defect synthesis.
• Design and train vision‑language and vision‑language‑action models that map work orders, CAD plans and sensor data to pixel‑level understanding.
• Lead auto‑annotation pipelines (active learning, self‑training, synthetic data) that scale to millions of frames and point clouds.
• Optimize and compress models (INT8, LoRA, distillation) for deployment on Jetson‑class edge devices under ROS 2.
• Own the full AI lifecycle—from problem definition and literature review to prototyping, evaluation and production hand‑off to perception and controls teams.
• Publish internal technical reports and external conference papers.
• Mentor interns and junior engineers, fostering research best practices.

Good Fit If You Have

• Passion for turning cutting‑edge research into real‑time robotics solutions.
• Experience publishing in top AI conferences or journals.
• Comfort collaborating across perception, controls and hardware teams.