Enes Gokce, Developer in State College, PA, United States
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Enes Gokce

Verified Expert  in Engineering

Bio

Enes is a data scientist with seven years of experience in machine learning and natural language processing (NLP). He has a demonstrated history of working with deep learning and extensive experience programming in Python and R. His areas of professional interest include generative AI, large language models (LLMs), and hybrid AI solutions with retrieval-augmented generation (RAG) systems. Enes is a US permanent resident.

Portfolio

Native AI
Python 3, SQL, Open-source LLMs, Large Language Model Operations (LLMOps)...

Experience

  • Statistics - 8 years
  • Machine Learning - 8 years
  • Natural Language Processing (NLP) - 8 years
  • Deep Learning - 6 years
  • Amazon SageMaker - 6 years
  • Large Language Model Operations (LLMOps) - 5 years
  • Vector Databases - 3 years

Availability

Part-time

Preferred Environment

Git, Visual Studio Code (VS Code)

The most amazing...

...application I've built is a RAG system with a vector database.

Work Experience

Data Scientist

2021 - 2024
Native AI
  • Developed novel and accurate NLP systems using generative AI and large language models (LLMs).
  • Worked on named entity recognition (NER), text summarization, and emotion classification systems.
  • Prepared and presented reports on the NLP AI engine for investors and client onboarding.
  • Developed R&D part of Pinecone vector database solution for the RAG conversational AI chatbot system.
  • Monitored NLP repositories' logs on AWS Cloud Monitor to ensure optimal performance of AI algorithms.
  • Worked closely with the product team, kept them updated, and prepared technical documentation.
  • Led a team to build a chatbot system by using the retrieval augmented generation (RAG) framework.
Technologies: Python 3, SQL, Open-source LLMs, Large Language Model Operations (LLMOps), Retrieval-augmented Generation (RAG), Scalable Vector Databases, PyTorch, GitHub, Amazon SageMaker, Amazon Bedrock

Building RAG System for Market Research Survey Data Analysis

A hybrid LLM-based retrieval augmented generation (RAG) system that allows users to extract insight from big survey datasets. With this system, the user can communicate and explore survey data better.

Tools: PostgreSQL vector database, Bedrock API, Claude 3 model, Mistral 7B model, word embeddings

Interview Question Generation System

It's a project that generates interview questions based on job descriptions posted on a job board website. My AI solution generated five interview questions from different categories for each job role category.

Tools: Amazon Bedrock, Amazon SageMaker, Claude 3, prompt engineering

Topic Understanding

Worked on topic generation for each document.
• Did literature review.
• Created R&D roadmap based on the literature review.
• Created a demo output.
• Communicated with shareholders about the feature development process.
• Delivered the R&D part of the solution for topic extraction and topic classification.
• Worked with the SWE team on the project deployment.
2015 - 2016

Master of Education Degree in Adult Education

University of Minnesota - Saint Paul, Minnesota, USA

2006 - 2013

Bachelor of Science Degree in Mathematics Education

Bogazici University - Istanbul, Turkey

Libraries/APIs

PyTorch

Tools

Amazon SageMaker, Git, GitHub

Platforms

Visual Studio Code (VS Code)

Languages

Sass, SQL, Python 3

Frameworks

Bedrock

Paradigms

Test-driven Development (TDD)

Other

Mathematics, Statistics, Machine Learning, Deep Learning, Natural Language Processing (NLP), Large Language Model Operations (LLMOps), Data Visualization, APIs, Vector Databases, Amazon Bedrock, Prompt Engineering, Vectorization, Open-source LLMs, Literature Review, Retrieval-augmented Generation (RAG), Scalable Vector Databases

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