AI Engineer – Generative AI / LLM Engineer

About 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.

AI Engineer – Generative AI / LLM Engineer

We are looking for an experienced AI Engineer to design, develop, and optimize cutting-edge Generative AI and on-device AI solutions. The ideal candidate should have strong expertise in LLMs, model fine-tuning, quantization, inference optimization, and scalable AI application development.

Key Responsibilities

• Design, build, and deploy AI/ML solutions using Large Language Models (LLMs) and Generative AI technologies.
• Fine-tune open-source and proprietary foundation models for domain-specific use cases.
• Work on model optimization techniques including quantization, pruning, distillation, and efficient inference.
• Develop Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases.
• Optimize models for edge/on-device deployment on low-resource hardware.
• Collaborate with product, platform, and application teams to integrate AI capabilities into products.
• Evaluate model performance using appropriate benchmarks and metrics.
• Stay updated with the latest advancements in AI, GenAI, multimodal AI, and edge AI ecosystems.
• Mentor junior engineers and contribute to technical design discussions.

Required Qualifications

• B.Tech / M.Tech in Computer Science, Artificial Intelligence, Machine Learning, or related field.
• 3-5 years of software engineering or AI/ML development experience.
• Strong programming expertise in Python.

Hands-on Experience With

• LLM fine-tuning
• Transformer architectures
• PyTorch / TensorFlow
• Embedding models and semantic search
• Vector databases (FAISS, ChromaDB, Pinecone)
• Experience in model quantization and optimization techniques (INT8, 4-bit, GGUF, ONNX, TensorRT, llama.cpp, etc.).
• Good understanding of RAG architectures and prompt engineering.
• Strong debugging, analytical, and problem-solving skills.

Preferred Skills

• Experience with multimodal AI systems
• Experience deploying models on edge/mobile/embedded devices.
• Exposure to Android/iOS AI deployment is advantageous.
• Publications, open-source contributions, or Kaggle/AI competition experience are a plus.