ADAS AI/ML system development Experts

KPIT; Pune District; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

KPIT in India is seeking an experienced AI/ML engineer to design and implement end-to-end autonomous driving models. The role emphasizes perception-to-control pipelines, multi-modal fusion with camera, radar, and LiDAR, and large-scale training with transformers. Strong Python/C++ skills and production deployment experience are essential. Work locations include Bangalore and Pune in a hybrid setup.

Qualifications

• 4-5 years (minimum) of experience in AI/ML system development.
• 4+ years in autonomous driving, robotics, or real-time perception systems.
• Hands-on experience with transformers, multi-modal learning, and large-scale model training.
• Experience deploying ML models in production systems.

Responsibilities

• Design and develop end-to-end autonomous driving models (perception-to-control).
• Build multi-modal architectures (camera, radar, LiDAR) using DL and transformers.
• Develop world foundation models for scene understanding and motion prediction.
• Implement self-supervised and large-scale representation learning pipelines.
• Engineer robust training pipelines for large-scale real-world and simulated data.
• Develop corner-case mining and adversarial robustness strategies.
• Optimize models for real-time deployment on GPU/embedded platforms.
• Integrate AI systems into safety-critical automotive frameworks.

Skills

Python

C++

PyTorch

Transformers

Multi-modal

Model training

Sensor fusion

Distributed training

Production deployment

Job description

Experince: 8 to 12 years

Work Location: Bangalore & Pune

Notice Period: immediaet to 30 Days

Key Responsibilities:

• Design and develop end-to-end (perception-to-control) autonomous driving models
• Build multi-modal architectures (camera, radar, LiDAR) using deep learning and transformer-based models.
• Develop world foundation models for scene understanding, motion prediction, and structured environment representation.
• Implement self-supervised and large-scale representation learning pipelines.
• Engineer robust training pipelines for large-scale real-world and simulated datasets.
• Develop corner‑case mining, adversarial robustness, and long-tail scenario handling strategies.
• Optimize models for real-time automotive deployment (GPU/embedded platforms).
• Integrate AI systems into safety‑critical automotive frameworks.

Required Qualifications

• 4-5 years (minimum) of experience in AI/ML system development.
• 4+ years in autonomous driving, robotics, or real-time perception systems.
• Strong expertise in Python and C++.
• Deep experience with PyTorch or similar frameworks.
• Hands‑on experience with transformers, multi‑modal learning, and large‑scale model training.
• Strong understanding of sensor fusion, state estimation, and vehicle dynamics.
• Experience with distributed training and high-performance computing environments.
• Experience deploying ML models in production systems.