Description

You will design, develop, and implement behavioral logic and decision-making frameworks for autonomous AI agents, while tuning LLMs for agentic tasks.

This role is on-site.

Responsibilities

• Implement LLM tuning techniques including fine-tuning, prompt engineering, and RLHF to optimize agent performance.
• Develop and maintain evaluation metrics and testing frameworks to assess agent behavior and reliability.
• Analyze LLM performance in agentic contexts and identify specific areas for architectural or model improvement.
• Collaborate with research scientists to explore novel approaches in agentic AI and large language model applications.
• Document design specifications, implementation details, and experimental results for internal review and knowledge sharing.

Required Skills

• 8+ years of experience in software engineering, with a strong focus on AI/ML systems.
• Hands-on experience with large language models (LLMs) and their application to complex tasks.
• Proficiency in Python and experience with Hugging Face Transformers, PyTorch, or TensorFlow.
• Experience with Google Gemini or Vertex AI toolsets is preferred.
• Solid understanding of reinforcement learning principles, with practical experience applying them to real-world problems.
• Master's or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Strong programming skills and ability to debug complex AI systems.

Preferred Skills

• Experience building agents using Google Gemini Vertex toolset.
• Familiarity with simulation environments for training and evaluating AI agents.

Key Skills

Education

Any Graduate