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Mid-level ML role in Bengaluru with common ML and edge skills attracts high applicant density.
Automotive edge AI and embedded deployment require domain expertise, limiting cross-industry transferability.
Explicit 5–7 year requirement plus mandatory ML, edge/embedded and orchestration skills increases filter strictness.
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Develop and deploy advanced AI/ML models including Small Language Models (SLMs), Vision-Language/Audio (VLA/VLM) models, and AI orchestration layers targeting automotive edge and cloud environments.
Build multi-modal and cross-domain intelligence systems for in-vehicle conversational AI, context-aware reasoning, scene understanding, and sensor fusion spanning cockpit, ADAS, and connected services.
Design and implement ML pipelines and MLOps workflows ensuring safety, cybersecurity, and automotive standards compliance while collaborating across embedded software, cloud, and system integration teams.
Work Experience Required: 5-7 years in AI/ML engineering.
Strong proficiency in AI/ML techniques including deep learning, NLP, computer vision, LLM/SLM fine-tuning, and multi-modal model experience (VLM/VLA).
Programming skills: Python mandatory, C++ preferred; experience with PyTorch or TensorFlow frameworks.
Experience deploying AI models on embedded/edge platforms and cloud environments (Azure, AWS, GCP).
Experienced in agentic AI and orchestration system development using frameworks such as LangChain, Semantic Kernel, or custom AI pipelines.
Familiar with automotive domain standards and practices, including safety, cybersecurity, and potentially AUTOSAR or embedded systems.
Skilled in end-to-end AI model lifecycle management including MLOps, CI/CD, and data pipelines in an automotive context involving edge and cloud integration.