AI Modeling Specialist Engineer

Description and Requirements 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. Preferred Qualifications: 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. We follow a friendly hybrid model with three days a week in the office—great for collaboration and connection! We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.