Senior AI Engineer

Medpace, Inc.•Cincinnati, OH

About The Position

Do you have a passion for Artificial Intelligence (AI) and are looking to make a difference in how the latest clinical trials progress into the market? Do you have a background in data science and/or computer science with experience working with AI tools for interactive AI applications across various IT systems? If so, we have an exciting opportunity for you at Medpace. We are currently seeking a Senior professional with experience with artificial intelligence (AI) tools such as NLP, LLM, Agentic AI etc. This professional will be helping educate, program and fine-tune various AI tools, help IT teams to implement them into new applications, and lead a team of AI Engineers. This professional will work collaboratively across the organization with multiple teams to improve the efficiency of company processes and provide insights and assistance to users.

Requirements

Bachelor’s degree or higher in Artificial Intelligence, Computer or Data Science, or related field. Master’s degree preferred;
5 + years of experience implementing and fine-tuning Large Language Models or interactive AI applications;
Expert proficiency in programming languages and frameworks commonly used in NLP and AI (e.g., Python, TensorFlow, PyTorch);
Worked with different cloud platforms (e.g. Azure, AWS, GCP) and different AI tools (e.g. LLMs, Agentic AI etc.);

Responsibilities

Design, implement, and deploy machine learning models and systems to solve complex problems and drive business outcomes;
Research, develop, and implement AI tools such as NLP, LLM, and Agentic AI;
Supervise and support the AI infrastructure needs, working with the development teams to identify and provide the best AI capabilities possible that can be embedded into front-end applications;
Supervise the collection, preprocessing, and curation of large datasets required for training generative models;
Experiment with different machine learning techniques and algorithms, including supervised, unsupervised, semi-supervised, reinforcement, and deep learning;
Design and optimize machine learning pipelines and workflows, incorporating techniques for data cleaning, feature engineering, model selection, and hyperparameter tuning; and
Develop scalable and efficient machine learning infrastructure and systems for training, testing, and deploying models in production environments.