About the role
Responsibilities:
- 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 optimise machine learning and deep learning models, including NLP solutions.
Requirements:
- Bachelor's 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.