Senior AI/ML Engineer

Built an AI quotation and recommendation platform that interprets customer requirements, predicts relevant products and line items, drafts proposals through an LLM layer, and routes output through human approval workflows. Developed and evaluated recommendation, classification, and prediction models using feature engineering, imbalance handling, hyperparameter tuning, cross-validation, and metrics including precision, recall, F1 score, and ROC AUC.

Developed production RAG systems covering OCR, document parsing, chunking, embedding generation, vector search, reranking, and LLM inference, reducing manual document-processing effort by 80%. Built LangChain and LangGraph agent workflows with tool calling, structured outputs, guardrails, retries, fallbacks, and low-confidence human review.

Created MLOps capabilities with GitHub Actions, Jenkins, MLflow, staging, canary releases, automated rollback, drift detection, and automated retraining, reducing deployment failures by 40%. Deployed containerized AI services on AWS, Azure, and GCP using Docker, Kubernetes, Terraform, Python, and FastAPI. Led a team of five engineers and interns through sprint planning, code reviews, mentoring, and stakeholder collaboration.

Validated and evaluated large-scale annotated datasets against model performance benchmarks, identifying systematic data-quality issues affecting downstream model accuracy. Applied statistical anomaly detection and data-mining approaches to production data pipelines to identify irregular patterns and improve workflow reliability.

Performed root-cause analysis on pipeline errors and rewrote standard operating procedures and data-quality guidelines, improving annotation accuracy by 20%. Recognized as a Top Performer for handling high case volumes while maintaining more than 99% accuracy.

Completed a Master of Science in Data Science and Big Data Analytics with a GPA of 9.39.

Completed a Bachelor of Science in Computer Science with a GPA of 9.78.