Machine Learning Engineer | Enterprise Voice AI & B2B SaaS | LLMs, PyTorch, MLOps

I build production AI systems that turn machine learning research into reliable business infrastructure. My current work sits at the intersection of enterprise voice AI, LLM safety, ML operations, and full-stack product engineering.

I have helped integrate TrustWise into Voice AI serving 60K+ restaurants, contributing to a 20% drop in escalations through safer, more reliable AI behavior in production environments.

• Production Machine Learning: model integration, inference workflows, monitoring, evaluation, and reliability
• LLMs & AI Safety: trust, guardrails, escalation reduction, and enterprise AI behavior improvement
• MLOps & Data Infrastructure: Python services, SQL diagnostics, Datadog monitoring, Kubernetes/OpenLens workflows, and pipeline validation
• B2B SaaS Products: scalable web apps, backend systems, analytics dashboards, and customer-facing AI tools
• Applied ML Engineering: churn prediction, forecasting, recommendation systems, and computer vision experiments

Languages: Python, TypeScript, JavaScript, Java, R, SQL
ML / AI: PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LangChain, LLM evaluation
Backend: Node.js, FastAPI, Express, REST APIs
Frontend: React, Next.js, React Native, Tailwind CSS
Data / Cloud: Supabase, PostgreSQL, MySQL, Snowflake, AWS, GCP
DevOps / Observability: Git, Docker, Kubernetes, OpenLens, Datadog, Linux

Integrated AI trust and safety capabilities into enterprise Voice AI systems serving tens of thousands of restaurants, improving reliability and reducing escalations in production.

Built a churn prediction pipeline on the Kaggle Telco dataset with scikit-learn and XGBoost, comparing logistic regression and gradient boosting models with cross-validation, ROC AUC tracking, and feature importance analysis.

Developed a full-stack web app for side-by-side LLM comparisons, performance metrics, and analytics dashboards for evaluating model behavior and output quality.

Built a campus event and engagement platform focused on helping students discover events, increase participation, and improve campus community engagement.

• Building stronger production ML and ML infrastructure systems
• Deepening expertise in LLM evaluation, AI safety, and model serving
• Shipping B2B SaaS products with real business value
• Preparing for high-impact ML engineering and software engineering roles

The College of Idaho
B.A. in Computer Science
Minors: Arts & Design, Sociology

• LinkedIn: https://www.linkedin.com/in/geraldakorli/
• Email: gerald.akorli@gmail.com
• LeetCode: https://leetcode.com/BravoClassic