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Mid-level ML role, metro Bangalore, strong employer brand and broad AI/edge skillset attract many applicants.
Automotive edge deployment, AUTOSAR, and safety standards require domain-specific experience, limiting transferability.
Explicit 3–5 years, mandatory Python/ML stack, automotive and edge deployment requirements increase filtering.
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Develop and deploy Small Language Models (SLMs), Visual Language Models (VLM/VLA), and AI orchestration layers on edge and cloud platforms to enable intelligent automotive systems like cockpit, ADAS, and connected vehicles.
Build and optimize multi-modal AI systems integrating voice, visual perception, and sensor fusion for cross-domain automotive intelligence.
Design and implement ML pipelines and MLOps workflows compliant with safety, cybersecurity, and automotive standards while collaborating with embedded software, cloud, and HMI teams.
3-5 years of experience in AI/ML engineering, especially with deep learning, NLP, and computer vision.
Strong programming skills in Python (mandatory) and preferably C++.
Experience deploying AI models on embedded/edge devices and cloud environments (Azure, AWS, GCP).
Experience with AI/ML frameworks like PyTorch or TensorFlow and knowledge of model lifecycle management and CI/CD pipelines.
Has domain experience or strong familiarity with automotive systems like ADAS, cockpit, or connected vehicle technologies.
Possesses hands-on experience with agentic AI, AI orchestration frameworks (e.g. LangChain, Semantic Kernel), and multi-modal AI models.
Comfortable working end-to-end on AI engine development for embedded platforms using NPU, CPU, GPU and integrating AI components across system teams.