Artificial Intelligence Engineer

Job Description

Model Fine-Tuning: Fine-tune LLMs (e.g., OpenAI, Hugging Face, open-source models) using domain-specific datasets and advanced techniques (LoRA, PEFT, QLoRA). Prompt Engineering & Optimization: Design, test, and refine prompts to maximize output quality, consistency, and structure. Embedding Models: Build and utilize embedding-based systems (e.g., OpenAI Embeddings, Sentence-BERT) for semantic matching, clustering, and vector search. Data Pipeline Development: Design scalable pipelines for ingesting, cleaning, and preparing unstructured text data for model training and inference. Experimentation & Research: Rapidly prototype, benchmark, and iterate on new approaches using state-of-the-art LLM and NLP techniques. Model Deployment: Optimize models for real-time or batch inference in production environments, with a focus on speed, cost-efficiency, and scalability. Cross-functional Collaboration: Work closely with product and engineering teams to integrate AI models into production workflows. Research & Innovation: Stay abreast of the latest developments in AI and machine learning to continuously improve our analytical capabilities.