AI Engineer

LLM & Conversational AI: Research, fine-tune (SFT, LoRA/PEFT, RLHF/DPO), and evaluate LLMs for natural, proactive, and personalized dialogue. Handle multi-intent understanding and multi-zone control within a single command, and implement RAG and Knowledge Bases for automotive domains (user manuals, warranty & maintenance, traffic laws, POIs). Multi-agent Systems: Design architectures for agent orchestration, function/tool calling, planning, and memory. Manage routing between domains (vehicle control, navigation, knowledge, entertainment) and resolve task conflicts. Edge AI & Optimization: Perform model compression (quantization, pruning, knowledge distillation) and optimize on-device inference for low latency and offline capability using TensorRT, ONNX, or TFLite, balancing model quality against hardware constraints. Personalization & Proactivity: Build Context Engines and recommendation models to proactively suggest routes, charging stations, driving modes, HVAC, and entertainment content based on context and user habits, learning continuously from real-world feedback. Safety & Quality: Develop AI Guardrails to control hallucinations, block sensitive content, and protect personal data. Build evaluation benchmarks per feature and ensure stable production operation (MLOps/LLMOps). Requirements Experience: Minimum 3 years of hands-on experience in AI projects, specifically in LLM/NLP and Agentic systems. Core Technical Skills: Mastery of Transformer/LLM architectures, fine-tuning, RAG, function calling, and prompt & context engineering. Proven experience designing multi-agent orchestration, tool use, planning, and memory with output-quality control. Edge Deployment: Practical experience optimizing and deploying models on edge/embedded devices using TensorRT, ONNX Runtime, or TFLite. Software Engineering: Proficiency in Python and frameworks such as PyTorch, HuggingFace, and vLLM. Experience bringing models to production: MLOps/LLMOps, containerization, model serving, and monitoring. Education: Bachelor's degree or higher in Computer Science, IT, Data Science, Applied Mathematics, or related fields. Strong technical English proficiency. Preferred Skills (Plus): Experience in Speech (ASR, TTS, Voice Cloning, Wake-word Detection, Voice Biometrics), Computer Vision (object detection, driver/occupant monitoring, video understanding), or the Automotive/IVI/Embedded/real-time domain. Experience shipping large-scale LLM/Multi-agent products, publications at top-tier conferences (NeurIPS, ICML, ACL, CVPR, INTERSPEECH, etc.), or open-source contributions are highly valued.