13 production-grade AI & ML systems built for healthcare, enterprise, and consumer industries — with real metrics, architecture decisions, and business impact.
Designed and deployed a production-grade Retrieval-Augmented Generation system at Ascension Via Christi Health, enabling clinical teams to query 100K+ enterprise documents in natural language with real-time, citation-grounded answers.
Built a LangGraph-powered multi-agent orchestration system where specialized AI agents autonomously decompose complex clinical and enterprise tasks, delegate subtasks, use tools (web search, code execution, database queries), and produce verified outputs — mimicking a high-performing analyst team.
Engineered a reusable end-to-end fine-tuning pipeline for domain adaptation of open-source LLMs (Llama 2, Mistral) using PEFT techniques. The pipeline handles data curation, quantization, LoRA adapter training, evaluation with ROUGE/BLEU, and automated publishing to HuggingFace Hub.
Built a HIPAA-compliant conversational AI assistant for clinical teams at Ascension. Combines GPT-4 with a RAG layer over structured EHR data and clinical guidelines, enabling care teams to query patient history, surface relevant protocols, and generate draft clinical summaries in real time.
Engineered a HIPAA-compliant NLP pipeline that ingests unstructured clinical notes, discharge summaries, and radiology reports, then extracts structured entities (diagnoses, medications, procedures) and generates concise summaries. Deployed on Apache Spark for hospital-scale throughput.
Built a production semantic search platform combining dense bi-encoder embeddings (Sentence Transformers) with BM25 sparse retrieval using Reciprocal Rank Fusion. Deployed on Pinecone and Weaviate with an A/B testing framework that continuously improves ranking models using implicit user feedback signals.
Designed an AI-powered knowledge graph platform that ingests documents, extracts entities and relationships using BERT-based NER, builds a Neo4j graph, and exposes a GraphQL API enabling LLMs to reason over structured enterprise knowledge — bridging unstructured text and graph traversal.
Built an enterprise demand forecasting platform for Colgate-Palmolive covering 5,000+ SKUs across 12 global markets. Ensemble of XGBoost, LightGBM, and LSTM with automated feature engineering (lag features, rolling statistics, calendar events) and Airflow-orchestrated weekly retraining cycles.
Engineered a streaming anomaly detection platform processing Kafka event streams at 50K events/sec. Ensemble of Isolation Forest, Autoencoder, and statistical control charts with adaptive thresholds — triggers automated Slack/PagerDuty alerts and writes flagged events to a Delta Lake audit table.
Developed an automated document digitization system combining Vision Transformers (ViT) for document layout understanding with Tesseract + PaddleOCR for character recognition. Handles handwritten forms, multi-column medical PDFs, and low-quality scans — extracting structured JSON output for downstream systems.
Architected a complete ML lifecycle management platform — from experiment tracking (MLflow) and model registry to canary deployments on Kubernetes, automated drift detection (evidently.ai), and a Grafana observability stack. Standardized the path from notebook to production across 3 engineering teams.
Built an event-driven ML inference platform on AWS where Kafka topics trigger real-time model scoring via SageMaker endpoints. Features horizontal autoscaling (response to traffic spikes in <90s), live Grafana dashboards, and a shadow-mode framework for safely validating new model versions before full cutover.
Designed a fault-tolerant ETL platform ingesting data from 8 source systems (EHR, lab systems, billing, scheduling) into a unified Snowflake data warehouse. Built with dbt for transformation lineage, Apache Airflow for orchestration, and great_expectations for automated data quality gates — serving as the foundation for all downstream ML models.
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