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Mid-level ML role, metro location, and popular AI title drive high candidate competition.
Automotive edge deployment and safety requirements create strong domain bias, reducing cross-industry transferability.
Explicit 5-7 years plus mandatory ML stack, edge deployment, and safety constraints make filters highly strict.
Develop and deploy AI/ML models including Small Language Models (SLMs), Visual Language Models (VLM/VLA) for in-vehicle conversational AI, scene understanding, and multi-modal perception across edge and cloud environments.
Build AI orchestration layers for intent and tool routing, policy management, and cross-domain context integration relevant to automotive intelligence (cockpit, ADAS, connected vehicle).
Design and implement ML pipelines and MLOps workflows while ensuring compliance with safety, cybersecurity, and automotive standards; collaborate with embedded software, cloud/data engineering, and HMI teams.
5-7 years of experience in AI/ML engineering focused on deep learning, NLP, computer vision, and large or small language model fine-tuning.
Strong programming skills in Python (mandatory) and preferably C++ with experience in PyTorch or TensorFlow frameworks.
Experience deploying AI models on embedded/edge platforms and cloud environments (Azure, AWS, GCP).
Work Experience Required: 5-7 years relevant AI/ML experience in automotive or related AI domains.
Proven expertise in agentic AI systems and orchestration frameworks such as LangChain or similar for multi-agent LLM applications.
Experience working on automotive domain AI projects, especially those related to ADAS, in-vehicle conversational AI, or connected vehicle intelligence.
Demonstrated ability to develop and deploy AI solutions seamlessly across edge devices with NPUs, CPUs, GPUs and integrate across software stacks involving embedded, cloud, and HMI systems.