SUMMARY

AI/ML Engineer with proficiency in NLP , Large Language Models (both with fine-tuning & prompting) and Speech Processing.

Possessing strong software engineering background that includes writing typesafe code with pydantic/mypy, unit & integration tests, creating GraphQL APIs, and CI/CD processes.

PRIMARY WORK EXPERIENCE

LEADIQ - Machine Learning Engineer (January 2022 – Present; San Francisco, CA)

Tech Stack : PyTorch, GPT , OpenAI API, Strawberry, FastAPI, Flask, Streamlit, Transformers, HuggingFace, Langchain, Gitlab, Linux, AWS, MongoDB, Docker, Kubernetes, Datadog, Insomnia, Sisense, Redis, Redshift, Databricks, PySpark, Slack, Jira

Developed personalized prospecting email generator using Large Language Models (LLMs) that helped users to reach out to >5x more prospects with competitive open & reply rates
Improved the quality of emails generated by utilizing automated user feedback to either fine-tune or improve prompting of our LLMs
Utilized with many different fine-tuning techniques to increase performance including upside-down RL / decision transformer style, weighting of user feedback data, various sampling strategies to reduce entropy collapse and improve variety
Improved stability of Databricks pipeline that computes open & reply rate statistics daily.
Trained a job title seniority & function classifier using bi-directional LSTMs, which replaced the old rule-based model and is used widely in production which achieved >90% classification accuracy among 6 seniority classes and >70% accuracy among 50 job functions that was used by the most central services in the company
Trained a job title similarity scorer with a Siamese network achieving >95% classification accuracy
Designed and developed APIs with GraphQL, wrote typesafe code with pydantic/mypy, wrote integration tests and unit tests, reviewed merge requests, inspected and resolved errors from datadog logs and customer complaints, collaborated with project manager and other team members
Refactored existing code for performance; for example re-wrote a code snippet that finds the ticker of a company given its domain, helping us increase our precision by absolute 15%

SESTEK - Senior AI Research Engineer (March 2020 – December 2021; Istanbul, Turkey)

Tech Stack : PyTorch, BERT , LSTM, FastAPI, Transformers, HuggingFace, Linux, Docker, Azure DevOps, Azure Cloud

Fine-tuned kaldi library for task specific speech recognition models.
Worked with different types of model architectures (CNNs, RNNs etc.) for various speech classification tasks (age & gender & emotion detection, spoofing detection for voice biometrics etc.)
Conducted comprehensive literature reviews and wrote reports to ensure products stayed up-to-date
Organized regular meetings with auditors for R&D projects to ensure the continuation of the investments
Created a parent Python project for Machine Learning (ML) model pipeline to automate the training and deployment process which evolved into SestekAI, a product helping customers train and serve their models in their local environments

SESTEK - Research and Development Engineer (March 2017 – March 2020; Istanbul, Turkey)

Trained intent detection and FAQ text classifier models using PyTorch, Hugging Face libraries, LSTM, and Google BERT architectures, delivering 85% intent detection accuracy to the chatbot application of one of Turkey’s largest banks
Developed a sequence-to-sequence LSTM model that efficiently transformed textual representations of numeric values such as addresses, dates, and times into formatted forms, leading to a publication on this subject
Took part in every stage of the ML Pipeline: data cleansing, training, evaluation, testing on real customer data, and deploying trained models as REST services using Python

EDUCATION

MSc - BOGAZICI UNIVERSITY - Electrical and Electronics Engineering (GPA: 3.69)

Research Focus: Automated Response Generation for Corporate Chatbot Systems
Specialized Courses: Pattern Recognition, Speech Processing, ML, Statistical Signal Analysis, Social Semantic Web

BSc - BOGAZICI UNIVERSITY - Electrical and Electronics Engineering (GPA: 3.28)

Core Courses: Probability, Matrix Theory, Signal Processing, Introduction to Image Processing

TECHNOLOGIES

Python : PyTorch, Langchain, OpenAI API, Strawberry, FastAPI, Flask, Streamlit, Chainlit, Numpy, Pandas, Pydantic etc.
AI/ML : GPT , Transformers, LSTM, BERT , Hugging Face, Llama, LlamaIndex, Mlflow
Data Analytics : Databricks, PySpark, Redshift
CI/CD : Gitlab, Github, Docker, Kubernetes, Datadog, Insomnia, Redis, Slack, Jira, AWS, MongoDB, Azure DevOps
Other : Linux, Sisense

PUBLICATIONS

A. S. Güser, M. Erden and M. L. Arslan, “Semi-Automatic Formatting of Spelled Out Numbers” 2019 27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey, 2019

CERTIFICATES

ADDITIONAL

Achievements : Ranked 6th among 1.7m students in the National University Entrance Exam (2012)
Activities : Walking, Literature, and Traveling

Abdullah Şamil Güser