AI Modeling Specialist Engineer

This role will focus on developing and optimizing AI models that solve real business problems across the organization. The engineer will work on the end-to-end AI model lifecycle, including data preparation, model training, fine-tuning large language models (LLMs), prompt engineering, evaluation, deployment, and continuous improvement. They will collaborate closely with software engineers, data engineers, and business stakeholders to deliver scalable, production-ready AI solutions that drive measurable business value.

Job Responsibilities:

1. Responsible for training, fine-tuning, and optimizing large-scale language and multimodal models (e.g., vision-language, video-text models);

2. Build robust training pipelines including data processing, distributed training, checkpointing, and deployment;

3. Contribute to building enterprise-level AI platforms and explore real-world applications such as product recommendation, semantic search, or multimodal understanding;

4. Track and apply cutting-edge research in LLMs and multimodal learning to improve model performance;

5. Collaborate closely with algorithm, product, and data teams to accelerate AI transformation across the business.

Basic Qualifications:

• Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field (or equivalent experience).
• Strong programming skills in Python.
• Solid understanding of machine learning, deep learning, and neural networks.
• Experience with PyTorch, TensorFlow, or similar ML frameworks.
• Hands-on experience with Large Language Models (LLMs) and prompt engineering.
• Experience with model evaluation, fine-tuning, and optimization techniques.
• Knowledge of data preprocessing, feature engineering, and model validation.
• Strong problem-solving skills and ability to communicate complex technical concepts.

• Experience with Retrieval-Augmented Generation (RAG).
• Fine-tuning open-source LLMs (e.g., Llama, Qwen, Mistral, DeepSeek).
• Experience with multimodal AI models.
• Familiarity with AI Agent frameworks such as LangGraph, AutoGen, or CrewAI.
• Knowledge of distributed training and GPU optimization.
• Experience with vector databases and semantic search.
• Familiarity with MLOps tools and model deployment pipelines.
• Publications, open-source contributions, or participation in AI research projects.