AI/ML Engineer - LLM Tuning & Agentic Behaviour

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.

Qualifications:

• 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, TensorFlow, 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.  

Bonus Points:

• 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.