Back to Portfolio
Industry Case Studies

All Projects

13 production-grade AI & ML systems built for healthcare, enterprise, and consumer industries — with real metrics, architecture decisions, and business impact.

13Projects shipped
40%Avg accuracy gain
50K+Docs processed
3Cloud platforms
$2M+Cost savings delivered
98%Highest model accuracy
<100msBest inference latency
60%Max compute cost cut
HIPAACompliance achieved
5+Autonomous AI agents
13 projects
Generative AI
Enterprise RAG System — AI knowledge retrieval interface
Generative AI Production
01

Enterprise AI Knowledge Retrieval (RAG)

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.

"Reduced average time-to-answer for clinical staff from 8 minutes to under 40 seconds — enabling faster care decisions at the bedside."
Ascension Via Christi Health Sep 2024 – Present HIPAA Compliant
35%Accuracy boost
<1sQuery latency
100K+Docs indexed
LangChainFAISSAzure OpenAIFastAPI DockerPineconePythonRedis
Multi-Agent AI Orchestration — neural network visualization
Generative AI Agentic AI
02

Multi-Agent AI Orchestration Framework

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.

"5 specialized agents collaborating in real-time reduced task resolution time by 65% on complex multi-step workflows compared to single-agent approaches."
5 Specialized Agents LangGraph DAG Tool Use + RAG
5+AI agents
65%Faster resolution
Task types
LangGraphLangChainGPT-4FastAPI RedisDockerKubernetes
LLM Fine-Tuning Pipeline — model training visualization
Generative AI Fine-Tuning
03

LLM Fine-Tuning Pipeline (LoRA / QLoRA)

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.

"Achieved 60% GPU memory reduction using 4-bit QLoRA quantization while maintaining 96% of full fine-tune benchmark scores — making domain LLMs accessible on a single A100."
QLoRA 4-bit Quantization Custom Domain Datasets ROUGE / BLEU Eval
60%Cost reduction
96%Benchmark score
4-bitQuantization
LoRAQLoRAPEFTPyTorch HuggingFaceLlama 2MistralW&B
Healthcare AI Diagnostic Assistant — clinical AI interface
Generative AI Healthcare
04

Healthcare AI Diagnostic Assistant

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.

"Reduced documentation time for nursing staff by 28% during pilot — flagged for enterprise-wide rollout across 3 hospital networks."
HIPAA Compliant EHR Integration Clinical Workflows
28%Less doc time
3Hospital networks
99.9%Uptime SLA
GPT-4LangChainFAISSAzure FastAPIPostgreSQLHL7 FHIR
NLP & Language Intelligence
Clinical NLP Document Intelligence — medical records processing
NLP Healthcare
05

Clinical NLP Document Intelligence

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.

"Processed 50,000+ patient documents in the first production run — reducing manual chart review effort by 30% across the care coordination team."
50K+ Records NER + Summarization Spark Distributed
30%Manual effort saved
50K+Docs processed
F1 0.91NER accuracy
BERTTransformersspaCyApache Spark PythonHugging FaceMLflow
Knowledge Graph AI System — graph database visualization
NLP Graph AI
07

Knowledge Graph AI System

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.

"Enabled analysts to answer multi-hop questions across 200K+ entity relationships that previously required 3 separate database queries and manual joining."
Neo4j Graph DB Entity Linking GraphQL API
200K+Entities mapped
F1 0.88Relation extract
3xQuery speed
Neo4jLlamaIndexNERBERT GraphQLPythonspaCy
Machine Learning
Demand Forecasting ML System — analytics dashboard
Machine Learning Colgate-Palmolive
08

Demand Forecasting ML System

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.

"20% improvement in forecast MAPE over the legacy statistical model — translating to ~$1.4M in reduced overstock and stockout costs annually."
Colgate-Palmolive 5K+ SKUs 12 Markets
20%MAPE reduction
$1.4MAnnual savings
5K+SKUs covered
XGBoostLightGBMTensorFlowAirflow MLflowPySparkSnowflake
Real-Time Anomaly Detection — monitoring dashboard
Machine Learning Real-Time
09

Real-Time Anomaly Detection System

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.

"Caught a data pipeline failure within 4 seconds of onset — previously undetected failures caused 6-hour data gaps impacting downstream analytics."
50K Events/sec PagerDuty Alerts Adaptive Thresholds
95%+Precision
50K/sEvent throughput
4sDetection lag
KafkaIsolation ForestPyTorch Autoencoder Scikit-learnFastAPIDelta LakeDocker
Computer Vision OCR Pipeline — document scanning AI
Machine Learning Computer Vision
10

Computer Vision OCR Pipeline

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.

"Reduced manual data entry for clinical forms from 4 minutes per document to 8 seconds — enabling same-day processing of incoming patient paperwork."
Multi-format Docs Handwriting Support JSON Output API
98%OCR accuracy
30xFaster than manual
8sPer document
ViTCLIPOpenCVTesseract PaddleOCRPyTorchFastAPI
MLOps & Infrastructure
End-to-End MLOps Pipeline — Kubernetes deployment infrastructure
MLOps Zero Downtime
11

End-to-End MLOps Platform

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.

"Reduced model deployment time from 3 days to 45 minutes — with 30% fewer post-deployment incidents thanks to automated drift alerting."
Kubernetes Canary Drift Detection 3 Teams Onboarded
45minDeploy time
30%Fewer incidents
100%Zero-downtime
MLflowKubernetesPrometheusGrafana CI/CDJenkinsevidently.aiDocker
Real-Time Streaming Prediction System — server infrastructure
MLOps AWS
12

Real-Time Streaming Prediction System

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.

"System handled 3x traffic spike during product launch (Black Friday equivalent) with zero degradation — inference latency stayed under 85ms at peak."
<85ms Inference Auto-scaling <90s AWS SageMaker
<85msP99 latency
3xTraffic spike handled
99.95%Availability
KafkaTensorFlowAWS SageMakerDatabricks GrafanaTerraformDocker
Scalable ETL Data Platform — data pipeline architecture
MLOps Data Engineering
13

Scalable Clinical Data ETL Platform

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.

"Reduced data pipeline latency from 6 hours to 22 minutes — enabling near-real-time clinical dashboards that inform daily bed management decisions."
8 Source Systems Data Quality Gates 22min Latency
94%Latency reduction
8Source systems
99.8%Data quality score
Apache SparkAirflowdbtSnowflake Delta Lakegreat_expectationsPython

Interested in my work?

I'm actively seeking roles in Generative AI, ML Engineering, and Data Science. Let's build something impactful together.

Get In Touch Download Resume