Software Engineer & AI Trainer specializing in refining, testing, and optimizing large language models and machine learning systems.
I am a passionate Software Engineer and AI Trainer dedicated to bridging the gap between raw data and intelligent, reliable AI systems. Living and working in the creative hub of Brooklyn, NY, I bring a detail-oriented and user-focused perspective to machine learning.
Over the past few years, I have successfully contributed to Over 10 major AI projects, handling everything from rigorous model training and prompt engineering to adversarial testing and QA. My hybrid background allows me to not only evaluate AI outputs for human-like alignment but also engineer the software pipelines that support these models.
AI Projects Completed
Years Experience
A selection of my recent AI training, testing, and software engineering work.
Conducted extensive RLHF (Reinforcement Learning from Human Feedback) on a medical diagnostic assistant. Evaluated thousands of prompts for safety, accuracy, and bedside manner.
Developed a Python-based testing framework to identify edge-case failures in an autonomous driving computer vision model, increasing detection rate of anomalies by 23%.
Led a team of annotators to adversarialy test a fintech customer support bot. Designed complex multi-turn conversation scenarios to expose logic flaws and PII leaks.
Curated and cleaned a massive dataset of social media posts across 5 languages to train a sentiment analysis model. Validated outputs against human baselines.
Combining software engineering principles with rigorous AI evaluation methodologies.
Guiding language and vision models to behave safely, accurately, and helpfully through meticulously crafted prompts and Reinforcement Learning from Human Feedback.
Systematically attacking AI systems to uncover vulnerabilities, bias, hallucinations, and safety breaches before models are deployed to production.
Building the robust scripts, APIs, and data pipelines necessary to support large-scale AI testing, dataset curation, and model evaluation processes.
Reviewing, cleaning, and structuring raw datasets to ensure high-quality inputs, as "garbage in" equals "garbage out" in machine learning.
My toolkit bridges traditional software development and modern AI ecosystems. I am comfortable writing code to automate workflows just as much as I am evaluating complex generative outputs.
Whether you need a software engineer to build out your tooling or an experienced AI trainer to refine your models, I'd love to chat. Based in Brooklyn, available everywhere.