AI Engineer

quest

Thiruvananthapuram, India NM Years Exp Posted 41d ago

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.

Work Experience

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