Enes Gokce
Verified Expert in Engineering
Full-stack Developer
Lancaster, PA, United States
Toptal member since October 9, 2020
Enes is a data scientist with eight years of experience in machine learning (ML) 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 (GenAI), large language models (LLMs), and hybrid AI solutions with retrieval-augmented generation (RAG) systems.
Portfolio
Experience
- Statistics - 8 years
- Machine Learning - 8 years
- Artificial Intelligence (AI) - 8 years
- Amazon SageMaker - 6 years
- Python - 5 years
- SQL - 5 years
- Large Language Model Operations (LLMOps) - 5 years
- Vector Databases - 3 years
Preferred Environment
Git, Visual Studio Code (VS Code), Python, AWS Bedrock AgentCore, Amazon SageMaker, Docker
The most amazing...
...application I've built is an Agentic AI system with a vector database that has become the most valuable intellectual property of the start-up I was part of.
Work Experience
NLP Data Scientist
Nativeagtech
- Developed novel and accurate NLP systems using generative AI (GenAI) and large language models (LLMs).
- Contributed to 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 the R&D part of the 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.
- Collaborated closely with the product team, kept them updated, and prepared technical documentation.
- Led a team to build a chatbot system using the retrieval augmented generation (RAG) framework.
Experience
Topic Understanding
• 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.
Interview Question Generation System
Tools: Amazon Bedrock, Amazon SageMaker, Claude 3, prompt engineering
Market Research Survey Data Analysis with Large Language Models (LLMs)
Some steps I completed in this project:
• Converted tabular data to textual data with text augmentation.
• Created a RAG system by using LangChain and Llamaindex frameworks
• Tested different implementation ideas that give the best results for this specific client.
• Evaluated LLM results using the LLM Evaluation Benchmark Rubric developed by the in-house data science team (human evaluation).
Building a RAG System for an Enterprise Client
Tools: PostgreSQL vector database, Bedrock API, Claude 3 model, Mistral 7B model, word embeddings
NLP Data Scientist
• Contributed to 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 the R&D part of the 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.
• Collaborated closely with the product team, updated them, and prepared technical documentation.
• Led a team to build a chatbot system using the retrieval augmented generation (RAG) framework.
Education
Master of Education Degree in Adult Education
University of Minnesota - Saint Paul, Minnesota, USA
Bachelor of Science Degree in Mathematics Education
Bogazici University - Istanbul, Turkey
Skills
Libraries/APIs
PyTorch
Tools
Amazon SageMaker, ChatGPT, Claude, Git, GitHub, Docker Compose
Languages
Python, SQL
Platforms
Visual Studio Code (VS Code), Amazon Web Services (AWS), AWS IoT, Docker
Storage
PostgreSQL
Paradigms
Test-driven Development (TDD)
Frameworks
LlamaIndex
Other
Mathematics, Statistics, Machine Learning, Deep Learning, Natural Language Processing (NLP), Vector Databases, Large Language Model Operations (LLMOps), Prompt Engineering, Vectorization, Open-source LLMs, Literature Review, Retrieval-augmented Generation (RAG), Scalable Vector Databases, Artificial Intelligence (AI), Large Language Models (LLMs), Generative Artificial Intelligence (GenAI), Generative Pre-trained Transformers (GPT), Anthropic, Neural Networks, AI Chatbots, Chatbots, Conversational AI, Fine-tuning, Data Visualization, APIs, Amazon Bedrock AgentCore, LangChain, ChatGPT API, Cloud Computing, SaaS, Startups, R&D
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