Technical Lead - AI/ML

What We offer:

• At our headquarters in Noida, you will find an innovative international automotive environment that is at the same time familiar and appreciative.
• Exciting tasks with interesting and secure prospects in a global company
• Attractive salary
• Very good professional and personal development
• Work with a cutting-edge team in a rapidly growing field. Collaborative environment with world-class experts in AI, imaging, and embedded systems.

Role & responsibilities:

• Lead the design, development, and optimization of AI/ML algorithms for ADAS functionalities such as object detection, lane detection, pedestrian recognition, and path planning.
• Develop multi-sensor fusion frameworks involving cameras, radars, LIDAR, and IMUs for perception and localization.
• Drive the deployment and inference optimization of ML models on edge computing platforms (e.g., NVIDIA Jetson, Qualcomm, TI, or custom SoCs).
• Mentor and guide a team of algorithm engineers and researchers to meet technical milestones.
• Collaborate with cross-functional teams (hardware, systems, validation, cloud) to ensure system-level performance and safety.
• Ensure alignment with ASPICE, ISO 26262, and other automotive standards.
• Conduct regular code and architecture

Preferred candidate profile:

• 6-10 years of experience in AI/ML algorithm development, with at least 5 years in the ADAS domain.
• Master's degree or higher in Computer Science, Electrical Engineering, or a related field. Ph.D. is a plus.
• Strong background in automotive (ADAS, DMS, object detection)
• Experience in leading algorithm integration into production ADAS ECU/software platforms.
• Excellent communication, leadership, and mentoring abilities.

Technical Skills

• Programming: Python, C++, TensorFlow, PyTorch, OpenCV.
• Algorithms: Image enhancement, super-resolution, denoising, segmentation, feature extraction.
• ML Techniques: CNNs, GANs, transformers for vision, classical computer vision.
• Domain Knowledge: Automotive standards (ISO 26262, ASPICE), DICOM, HL7, medical imaging formats.
• Optimization: Quantization, pruning, real-time inference on GPUs, NPUs, embedded systems.
• Tools: Docker, Kubernetes, CI/CD, cloud platforms (AWS, Azure, GCP).
• Data Handling: Large-scale image datasets, annotation workflows, synthetic data generation.
• Deployment: Edge devices (NVIDIA Jetson, Qualcomm, TI)