Description :

- Fine-tune LLMs using techniques like LoRA and QLoRA

- Evaluate and improve RAG (Retrieval-Augmented Generation) pipelines for groundedness, accuracy, and relevance

- Apply transfer learning and transformer architectures in model development

- Validate model accuracy and performance using appropriate metrics

- Collaborate with product teams and communicate insights to senior leadership

- Participate in problem-solving sessions and contribute innovative ideas

- Maintain an experimental mindset and continuously explore new approaches

- Identify and integrate relevant data sources to build meaningful datasets

- Automate data collection and preprocessing for structured and unstructured data

- Handle large-scale data to feed analytical and predictive models

- Build and optimize machine learning and deep learning models, including NLP solutions

Requirements :

Education & Experience :

- Bachelors degree in a quantitative field (Computer Science, Engineering, Physics, Mathematics, Operations Research) or equivalent experience

- 3 - 5 years of hands-on experience in Gen AI and NLP

- Prior experience in startups or high-growth environments is a plus

Technical Skills :

- Deep expertise in NLP techniques : text generation, sentiment analysis, NER, and language modeling

- Hands-on experience with LLMs and RAG pipelines

- Proficiency in neural network frameworks : TensorFlow, PyTorch

- Familiarity with transformer architecture and transfer learning

- Fluency in at least one programming language : Python, R, or Julia

- Experience with Gen AI libraries : Hugging Face, OpenAI, etc.

- Strong foundation in ML algorithms : supervised, unsupervised, reinforcement learning, Bayesian inference

- Fine Tuning, Transfer Learning, Pytorch, Tensorflow