AI/ML Engineer - Agentic Behaviour & LLM Tuning
Diamondpick
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, 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.
- 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.
Vacancy posted 1 day ago
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