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Mid-level ML role, metro Bangalore, and popular AI title increases candidate competition.
Role requires automotive edge expertise and compliance, making background fit highly industry-specific.
Multiple mandatory ML, edge deployment, orchestration, and automotive safety requirements make filters very strict.
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Develop and deploy 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 AI systems integrating voice, visual perception, and sensor fusion to enhance cockpit, ADAS, and connected vehicle functionalities.
Design ML pipelines and MLOps workflows ensuring compliance with safety, cybersecurity, and automotive standards while collaborating across embedded software, cloud, and HMI teams.
3-5 years of experience in AI/ML engineering focused on automotive or related AI systems.
Strong experience in deep learning, NLP, computer vision, and fine-tuning Large or Small Language Models.
Proficient programming skills in Python (mandatory) and preferably C++.
Experience deploying AI models on embedded/edge platforms and cloud environments (Azure, AWS, GCP).
Has practical experience with AI orchestration frameworks (e.g., LangChain, Langgraph, Autogent, Semantic Kernel) and agentic AI tool usage.
Familiarity with automotive domain technologies such as ADAS, cockpit systems, connectivity, and embedded AUTOSAR platforms is a plus.
Operates effectively in cross-disciplinary teams integrating embedded, cloud, and HMI software for end-to-end automotive AI solutions.