Job Requirements
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/ML Engineer to design, develop and deploy machine learning algorithms on embedded platforms for the next generation automotive infotainment systems.
In this pivotal role, you will develop solutions, design and lead solution implementations that enhance our offerings.
Roles & Responsibilities:
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Design, train, and optimize AI/ML models for real-time, edge-based deployment.
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Design, implement, and optimize Retrieval-Augmented Generation (RAG) workflows and fine-tune large language models (LLMs) to build scalable, secure, and production-ready enterprise GenAI applications.
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Work with multi-modal data sources (audio, vision, sensor, telematics) to build robust AI systems.
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Develop end-to-end pipelines for data preprocessing, model training, evaluation, and deployment.
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Implement algorithms optimized for embedded and resource-constrained platforms
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Validate AI models with real-world automotive datasets.
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Perform model optimization (quantization, pruning) for deployment on embedded SoCs
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Collaborate with system architects, Application developers, and automotive domain experts to ensure end-to-end functionality
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Stay up to date with AI/ML advancements and automotive industry trends.
Work Experience
Required Skills (Technical Competency):
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4–5 years of proven development experience in AI/ML for embedded device
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Strong expertise in deep learning frameworks (TensorFlow, PyTorch, ONNX, TensorRT).
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Expert proficiency with open-source infrastructure tools like llama.cpp, vLLM, Ollama, and NVIDIA Triton Inference Server for local model serving, high-throughput inference, and quantization.
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Hands-on experience building complex, stateful workflows and tracking applications using LangChain, LangGraph, and Langfuse.
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Expertise in selecting, benchmarking, and adapting state-of-the-art open-source architectures (such as Llama 3, gemma, Qwen, and Kimi) for enterprise tasks
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Practical experience with edge/embedded AI (NVIDIA Jetson, Qualcomm, ARM).
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Proficiency in NLP / Conversational AI / Speech interfaces ( ASR, TTS )
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Solid programming skills in Python and C++/Java for optimization and integration.
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Solid understanding of application development in embedded Linux / Android
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Familiarity with automotive standards, protocols such as CAN
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Familiarity with cloud ML services like AWS SageMaker or Azure ML
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Good communication skills and great team spirit.
Desired Skills:
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Experience working with embedded Linux, Automotive Android
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Knowledge of vehicle data interfaces (CAN, OBD-II, sensors).
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Exposure to LLMOps & MLOps pipelines and cloud platforms (AWS/GCP/Azure).
