Experience time
AI/ML Expert | Startup Technologist | Builder of Scalable, Intelligent Systems
I’m an AI/ML specialist with deep experience building intelligent systems that scale—from zero to production—in fast-paced startup environments. Over the past 8+ years, I’ve helped early-stage teams turn raw data into market-ready products using applied machine learning, cloud-native tools, and lean, iterative development.
At my core, I’m a builder. I thrive at the intersection of data science, software engineering, and product strategy—designing models that not only work, but fit the business. Whether it’s powering real-time personalization, predictive engines, or intelligent automation, I focus on delivering measurable value, fast.
Key Strengths:
Startup-Proven ML Expertise: Built and deployed ML systems from scratch—recommendation engines, customer scoring, NLP pipelines, and more.
End-to-End Delivery: From data ingestion and feature engineering to model training, evaluation, deployment, and monitoring.
Lean ML Operations: Agile ML development with rapid experimentation, A/B testing, and CI/CD automation.
Product-First Mindset: Partnered closely with founders, PMs, and engineers to ship features that drive growth and retention.
Responsible AI Advocate: Embedded fairness, explainability, and trust into every model we shipped.
Tech Stack: Python, PyTorch, TensorFlow, scikit-learn, SQL, AWS (SageMaker, Lambda), Docker, FastAPI, MLflow, dbt
I’m excited by bold ideas, messy data, and the chance to turn prototypes into production. Let’s build something impactful.
Work Experience
Education
Portfolio
LLM-Powered Conversational Assistant
Created a GPT-driven assistant for internal support and knowledge retrieval using LangChain and OpenAI APIs. Integrated vector database (Pinecone) for retrieval-augmented generation (RAG) to ground responses in company-specific documentation. Reduced average ticket resolution time by 40% and boosted internal tool adoption. Focused on prompt engineering, few-shot learning, and robust fallback mechanisms.
Credit Risk Modeling Platform
Developed ML models for credit scoring, fraud detection, and customer segmentation using XGBoost and autoencoders. Built MLOps pipelines using Airflow, Docker, and SageMaker for automated retraining and monitoring. Enabled 20% better risk stratification and significantly reduced false positives in fraud detection. Designed fairness-aware models to comply with financial regulations.