Eloquent AI Fellowship Program

Program Structure

• AI Engineering (6 weeks): Work on training, fine-tuning, and deploying AI agents that power enterprise-grade conversations. Gain hands-on experience in LLMs, RAG pipelines, prompt engineering, and inference optimization.
• AI Product Development (4 weeks): Collaborate with AI product teams to design, iterate, and integrate AI solutions into enterprise applications. Learn how to bridge the gap between cutting-edge AI research and real-world impact.
• Industry Applications & Capstone (2 weeks): Apply what you’ve learned in a real-world AI project, working with Eloquent AI’s product, engineering, and research teams to solve enterprise challenges.

Responsibilities

• Develop and deploy AI-powered agents, working with LLMs, RAG, and enterprise automation workflows.
• Gain hands-on experience in AI infrastructure, including LLMOps, MLOps, cloud deployment, and model optimization.
• Work on full-stack AI applications, collaborating with engineers and PMs to build scalable AI-driven products.
• Translate AI research into practical applications, integrating the latest advances in language models, embeddings, and retrieval techniques.
• Work directly with Eloquent AI’s leadership, learning from top AI engineers and product innovators.

Requirements

• Current or completed PhD or MSc degree in Computer Science, Engineering, Mathematics, Physics, or a related field.
• Strong mathematical foundation, particularly in statistics, linear algebra, and optimization techniques.
• Programming experience, ideally in Python, with familiarity in ML frameworks like PyTorch and TensorFlow.
• Interest in AI product development, data science, or machine learning engineering.
• Ability to work in a fast-paced, collaborative AI-driven environment.

Bonus Points If…

• Experience with LLMs, NLP, or Retrieval-Augmented Generation (RAG).
• Contributed to open-source AI projects or published research in AI/ML conferences (NeurIPS, ICML, ICLR, NLP, SIGIR, etc.).
• Hands-on experience with LLMOps, MLOps, cloud-based AI infrastructure, or AI deployment at scale.
• Experience in AI strategy, product management, or business applications of AI.