AI/ML Engineer-Automotive

Quest Global


Date: 6 hours ago
City: Thiruvananthapuram, Kerala
Contract type: Full time
Job Requirements

We are looking for an experienced AI/ML Engineer to design, develop and deploy machine learning algorithms on embedded platforms for the next generation automotive infotainment systems. This engineer will work on Android / Linux based applications that will process various sensor data and external input at real-time to predict various vehicle conditions and adjust vehicle operations accordingly.

Roles & Responsibilities

  • Design, train, and optimize AI/ML models for real-time, edge-based deployment.
  • Work with multi-modal data sources (audio, vision, sensor, telematics) to build robust AI systems.
  • Develop end-to-end pipelines for data preprocessing, model training, evaluation, and deployment.
  • Implement algorithms optimized for embedded and resource-constrained platforms
  • Validate AI models with real-world automotive sensor datasets
  • Perform model optimization (quantization, pruning) for deployment on embedded SoCs
  • Collaborate with system architects, Application developers, and automotive domain experts to ensure end-to-end functionality
  • Stay up to date with AI/ML advancements and automotive industry trends

Work Experience

Required Skills (Technical Competency):

  • 4–5 years of proven development experience in AI/ML for embedded device
  • Strong expertise in deep learning frameworks (TensorFlow, PyTorch, ONNX).
  • Practical experience with edge/embedded AI (NVIDIA Jetson, Qualcomm, ARM).
  • Proficiency in NLP / Conversational AI / Speech interfaces ( ASR, TTS )
  • Solid programming skills in Python and C++/Java for optimization and integration.
  • Solid understanding of application development in embedded Linux / Android
  • Familiarity with automotive standards, protocols such as CAN
  • Good communication skills and great team spirit

Desired Skills

  • Experience working with embedded Linux, Automotive Android
  • Knowledge of vehicle data interfaces (CAN, OBD-II, sensors).
  • Exposure to MLOps pipelines and cloud platforms (AWS/GCP/Azure).

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