• Completed a 6-week intensive bootcamp on Generative AI (GenAI) technologies, particularly Large Language Models (LLMs).• Gained hands-on experience with core AWS services like EC2, S3, DynamoDB, Lambda, and API Gateway.• Built common generative AI applications such as Question & Answer (Q&A) over data (RAG) and LLM agents.• Demonstrated proficiency in prompt engineering, fine-tuning for LLM optimization.• Collaborated effectively in a team environment through daily stand-ups… Show more • Completed a 6-week intensive bootcamp on Generative AI (GenAI) technologies, particularly Large Language Models (LLMs).• Gained hands-on experience with core AWS services like EC2, S3, DynamoDB, Lambda, and API Gateway.• Built common generative AI applications such as Question & Answer (Q&A) over data (RAG) and LLM agents.• Demonstrated proficiency in prompt engineering, fine-tuning for LLM optimization.• Collaborated effectively in a team environment through daily stand-ups, weekly progress demonstrations, and code reviews.• Independently explored online resources to deepen understanding of GenAI concepts and strengthen problem-solving skills.• Leveraged cloud platforms like Amazon Bedrock and Amazon Q to experiment with different LLMs.• Integrated LangChain frameworks (Langchain, LangServe, Langgraph) to build agentic applications and RAG pipelines.• Employed evaluation frameworks (LangSmith, TruLens) to assess the performance of custom RAG implementations.• Successfully deployed a production-ready RAG application at Emumba to facilitate efficient querying of internal company documentation using natural language. Show less

• Overview of Data Science and SQL• Stats and probability concepts• Dive into Python, manipulate data with Pandas, and peek into visualization• Machine Learning Fundamentals• Feature Engineering and Model Evaluation• Project and Final Presentation