Iranian Company in Tech Industry
Annual Package: 1.6 - 2 billion Tomans
Responsibilities
· Design and implement fine-tuning strategies for large language models (LLMs) to meet specific project requirements.
· Collaborate with cross-functional teams to identify and prioritize AI-driven solutions that enhance product functionality.
· Develop and maintain efficient pipelines for training, validating, and deploying LLMs in production environments.
· Conduct experiments to evaluate the performance of various fine-tuning techniques, including full fine-tuning and parameter-efficient methods like LoRA or adapters.
· Monitor model performance post-deployment, identifying areas for improvement and implementing necessary adjustments.
· Provide technical guidance and mentorship to junior engineers and data scientists on best practices in AI and machine learning.
· Stay updated on the latest advancements in LLM architectures and machine learning frameworks to inform development processes.
· Troubleshoot and resolve issues related to model training, deployment, and performance in production settings.
· Document processes, methodologies, and findings to ensure knowledge sharing within the team and organization.
· Participate in code reviews and contribute to the development of a robust codebase adhering to industry standards.
Requirements
· Proven experience in fine-tuning large language models using both full fine-tuning and parameter-efficient methods.
· Strong proficiency in Python programming, with hands-on experience in machine learning frameworks such as PyTorch or TensorFlow.
· In-depth understanding of LLM architectures, including their parameters and training dynamics.
· Experience with deployment strategies for machine learning models, including monitoring and maintenance practices.
· Familiarity with cloud platforms and tools for scalable model deployment and management.
· Ability to analyze complex datasets and derive actionable insights to inform model training and optimization.
· Strong problem-solving skills with a focus on identifying and mitigating common pitfalls in model training.
· Excellent communication skills, capable of conveying technical concepts to both technical and non-technical stakeholders.
· A degree in Computer Science, Data Science, Artificial Intelligence, or a related field; advanced degrees preferred.
· A collaborative mindset with a strong willingness to learn and adapt in a fast-paced, evolving environment.