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Mid-level ML role at a known automotive tech brand with popular skills and metro location.
Automotive edge AI, safety standards, and embedded deployment create high domain-specific constraints.
Explicit 5-7 years plus mandatory ML, edge deployment, safety/compliance and orchestration skills.
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Develop and optimize Small Language Models (SLMs), Visual Language Models (VLM/VLA), and AI orchestration layers for automotive AI systems across edge and cloud platforms.
Build multi-modal and cross-domain intelligence integrating voice, visual perception, and sensor fusion for cockpit, ADAS, and connected vehicle applications.
Design and implement ML pipelines and MLOps workflows ensuring compliance with safety and automotive cybersecurity standards, and collaborate with embedded and cloud engineering teams on end-to-end AI platforms.
5-7 years of experience in AI/ML engineering focusing on deep learning, NLP, computer vision, and multi-modal AI models.
Strong programming skills in Python (mandatory); C++ preferred; experience with PyTorch or TensorFlow frameworks.
Experience deploying AI/ML models on embedded devices (CPU, NPU, GPU) and cloud platforms (Azure, AWS, GCP).
Hands-on expertise with AI agent frameworks, orchestration tools, and ML lifecycle management (CI/CD, data pipelines).
Experienced in automotive domain AI applications such as ADAS, cockpit, and connected vehicle systems with knowledge of embedded and vehicle data platforms.
Proficient in developing and deploying real-time, edge-to-cloud AI pipelines including fine-tuning language and multi-modal models.
Capable of building integrated AI orchestration layers and agentic AI systems with cross-domain context management and tool routing.