• 
  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.