Description
Qualifications
- Bachelor's degree in Computer Science, Engineering, or related field.
- 2 years of experience in AI, with more than 4 years of hands-on experience in exploratory data analysis, data pipeline development, model development, and model evaluation
- Sound knowledge of theoretical and practical aspects of machine learning.
- Hands-on experience in developing deep learning models, preferably for Natural Language Processing applications.
- Experience with generative AI techniques and model fine-tuning.
- Proficiency in building pipelines for the use of externally hosted and locally hosted Large Language Models (LLMs).
- Comprehensive experience and detailed knowledge in implementing, enhancing, and evaluating RAG implementations/pipelines.
- Familiarity with orchestration layers such as LangChain for efficient use of LLMs.
- Experience with evaluation frameworks for Generative AI, particularly in text generation contexts (e.g., RAGAs and TruLens).
Responsibilities
- Conduct exploratory data analysis to understand data patterns and relationships.
- Develop robust data pipelines for preprocessing and feature engineering.
- Build and evaluate machine learning models for various applications.
- Develop deep learning models, particularly for Natural Language Processing tasks.
- Utilize generative AI techniques, including advanced prompting techniques and model fine-tuning.
- Build pipelines for the use of externally hosted Large Language Models (LLMs) and preferably locally hosted LLMs.
- Implement, enhance, and evaluate Retrieval-Augmented Generation (RAG) implementations and pipelines.
- Use orchestration layers such as LangChain for efficient utilization of LLMs.
- Experience with evaluation frameworks for Generative AI, specifically in the context of text generation (e.g., RAGAs and TruLens).
Skills
ML
NLP
Python
AWS
Data science
Industry Sector