AI​/ML Engineer - Agentic Behavior & LLM Tuning

Role: AI/ML Engineer - Agentic Behaviour & LLM Tuning Responsibilities

• Design, develop, and implement the behavioral logic and decision‑making frameworks for autonomous AI agents.
• Develop and maintain robust evaluation metrics and testing frameworks to assess agent performance and behavior.
• Conduct in‑depth analysis of LLM performance and identify areas for improvement within the context of agentic tasks.
• Implement various LLM tuning techniques, including fine‑tuning, prompt engineering, reinforcement learning from human feedback (RLHF), and other advanced methodologies.
• Collaborate with research scientists and other engineers to explore novel approaches in agentic AI and LLM applications.
• Contribute to the development of our AI platform and infrastructure to support the deployment and scaling of intelligent agents.
• Stay up‑to‑date with the latest advancements in AI, machine learning, and specifically in the areas of agentic systems and large language models.
• Document design specifications, implementation details, and experimental results.

• Master's or Ph.D. degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Strong foundation in the principles of artificial intelligence, machine learning, and deep learning.
• Proven experience in designing and implementing complex software systems, preferably in the context of AI agents or robotics. Good to have experience in building Agents using Google Gemini Vertex tool set.
• Hands‑on experience with large language models (LLMs) and their application to various tasks. Preference for Gemini LLMs.
• Experience with LLM tuning techniques and frameworks (e.g., Hugging Face Transformers, PyTorch, Tensor Flow, Vertex AI).
• Solid understanding of reinforcement learning principles and experience applying them to real‑world problems (experience with RLHF is a plus).
• Strong programming skills in Python and experience with relevant AI/ML libraries and frameworks.
• Excellent problem‑solving, analytical, and debugging skills.
• Strong communication and collaboration skills, with the ability 1 to effectively convey technical concepts to both technical and non‑technical 2 audiences.

• Experience with cognitive architectures or symbolic AI approaches.
• Familiarity with simulation environments for training and evaluating AI agents.
• Contributions to open‑source AI/ML projects.
• Publications in relevant AI/ML conferences or journals.

Chandler, AZ, United States