LLM Engineer

As LLM Engineer, the purpose of this role is to work with our team for the end-to-end deployment of LLMs to benefit our products.

Please find our research page : https://research.fynd.com/

Key responsibilities:

• Work with Python, LLM/GenAI frameworks and tools to create end to end ML pipelines from training to deployment
• Responsible for the end-to-end deployment of LLMs including scoping, testing, implementation, maintenance, tracking, and optimization of predictive models.
• LLM Model Fine-Tuning: Utilize your expertise in encoding and language understanding to fine-tune existing Large Language Models for optimal performance in solving domain-specific needs.
• Model Training and Evaluation: Conduct rigorous testing and evaluation of LLMs to identify areas for improvement and implement solutions to enhance their performance.
• Continuously research and stay up-to-date with recent advancements in NLP and large language models, applying novel techniques and methodologies to improve our models.
• CI/CD pipelines development, LLM model containerizing and deployment on cloud or premise. Models testing and follow-up maintenance. All stages of ML model life cycle ensuring and support.
• Design prototypes and POCs to demonstrate solution feasibility and value. Provide architecture solution.

Required Qualifications:

• Bachelor’s Degree (might be final course student) in Statistics, Applied Mathematics, Computer Science, or other related fields
• Proficiency in Python programming, with working experience over one of the ML Frameworks – SKLearn, XGBoost, PyCaret, and Deep Learning Frameworks – Tensorflow, PyTorch, Huggingface, and Rasa
• 1+ years of professional experience with NLP and Large Language Models (LLM)
• 2+ years of experience in implementing and deploying machine learning and deep learning frameworks on cloud platforms like AWS and GCP.
• Experience working with End to End Machine Learning project Life cycle .