AI Engineer
Quest Global
At Quest Global, it’s not just what we do but how and why we do it that makes us different. With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. Our people are driven by the desire to make the world a better place—to make a positive difference that contributes to a brighter future. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster.
We are looking for an experienced AI Engineer to lead requirement engineering and define the AI system architecture while executing the core responsibilities outlined below
Roles & Responsibilities
- Define requirements for in-vehicle services leveraging Generative AI and image/video data.
- Define technical requirements for AI-driven functions.
- Define vehicle data platforms and MLOps/DataOps architectures for continuous AI improvement.
- Collaborate with OEMs, Tier1 suppliers, cloud vendors, and AI engineering teams on technical alignment and requirement management.
- Manage requirement decomposition and traceability from system-level requirements to software specifications.
- Drive technical strategies for next-generation in-vehicle UX and AI agent integration.
- Define and architect multimodal AI capabilities, integrating voice (ASR/STT, TTS, NLP, RAG, LLMs) and vision-based intelligence to enable ADAS, driver monitoring, and next-generation in-vehicle cabin experiences
- Design hybrid LLM workflows using on‑device models for offline inference and cloud APIs (OpenAI, Gemini, etc.) for online operation.
- Implement personalization and contextual awareness features for enhanced user experiences.
- Optimize speech, NLP, and LLM pipelines for accuracy, latency, and reliability in automotive environments.
- Participate in requirements discussions, design reviews, feature planning, and technical decision‑making and support customer demos, technical presentations, and feature sign‑off discussions.
Required Skills (Technical Competency):
- Hands-on experience in requirement engineering.
- Strong understanding of Linux, Android platforms.
- Knowledge of cloud-connected architecture using AWS, Azure, or GCP.
- Understanding of AI/ML systems, especially computer vision and Generative AI technologies.
- Business-level communication skills in English for technical discussions.
- Understanding software lifecycle management, CI/CD, DevOps, and MLOps practices.
- Ability to drive projects across multiple stakeholders and organizations.
- Experience with LLMs, multimodal AI, or AI agent technologies.
- Practical experience implementing RAG pipelines (chunking, embeddings, retrieval, grounding).
- Experience working with vector databases (FAISS, Milvus, pgvector, etc.).
- Experience integrating local on‑device LLMs for offline use and cloud LLM APIs (OpenAI, Gemini) for online inference
- Strong capability to drive design reviews, requirement discussions, influence technical decisions, and clearly communicate complex technical concepts to both customers and non‑technical teams
- Experience in ADAS/AD, automotive camera systems, LiDAR, or Driver Monitoring Systems.
- Knowledge of edge AI platforms such as CUDA, TensorRT, and NVIDIA DRIVE.
- Experience utilizing digital twins and simulation environments (e.g., CARLA, SVL).
- Experience with ISO 26262, ASPICE, and ISO/SAE 21434 compliance activities.
- Knowledge of cloud-native technologies including Kubernetes, Docker, and Data Lake architecture.
- Experience in prototyping using Python and/or C++.
- Experience collaborating with automotive OEMs or Tier1 suppliers.
- Experience working in bilingual Japanese-English engineering environments.
- Promote architecture and requirement definition considering functional safety (ISO 26262) and cybersecurity (ISO/SAE 21434).
- Experience with model optimization techniques (quantization, pruning, distillation) for edge/IVI deployment.