Enhance customer experience and engagement by providing tailored product or personalized content suggestions.
Accomplishments:
Implemented collaborative filtering and content-based filtering techniques to generate personalized recommendations.
Led research and development of a prototype recommendation system based on Generative AI, integrating recommender models and Large Language Models (LLMs) to analyze user behavior and preferences, generating real-time, dynamic suggestions.
Transferable Skills:
Proficient in:
Machine Learning
Generative AI
Data Storage and Management (SQL, Elasticsearch)
Structured Data Extraction (Unstructured.io, GPT-4)
Data Preprocessing and Cleaning (Apache Spark, Pandas)
LLM Development Frameworks (Langchain, Llamaindex, Hugging Face Transformers, Google Vertex AI, Amazon SageMaker)
Vector Databases(Facebook AI Similarity Search - FAISS, Weaviate)
Experimentation with AI Pipelines (MLFlow)
Deployment and Scaling (Docker)
GitLab CI
Responsibilities:
Developed recommender systems for product recommendations.
Led the research and development of a Generative AI-based recommendation system prototype, enhancing personalized suggestions through the integration of LLMs.
Challenges and Difficulties:
Navigated challenges related to dataset quality, model convergence, and computational resource management, particularly in scaling the integration of LLMs with recommender systems.
