Sheikh
Khairul Momin
Mohammad Tahmid

Explainable AI Hiring Intelligence Platform

Modular 12-app Django REST API with JWT auth and role-based permissions, generating 384-dim SBERT embeddings to cosine-rank 3,000+ candidates per job posting. SHAP and LIME explanations are fused 60/40 into a unified explainability report per match, with disparate impact ratio and 4/5 rule computed across gender, ethnicity, and age groups. An async Celery pipeline orchestrates CV parsing, embedding generation, and batch matching, while a trainable GradientBoosting scorer ships with zero-downtime fallback to fixed weights.

TrialGuard: Clinical Trial Patient Dropout Prediction Platform

Django SaaS platform predicting clinical trial dropout up to 60 days in advance. XGBoost classifier with per-visit SHAP waterfall plots identifies at-risk patients at the individual level. Cox Proportional Hazards model delivers hazard ratios and 30/60/90-day retention probabilities, with stratified Kaplan-Meier curves segmented by risk tier (Low to Critical) via Lifelines. Cohort forecasts with 95% confidence intervals served through a DRF REST API; branded ReportLab PDFs generated per patient including SHAP feature drivers, survival curves, and clinical action logs.

Contract Intelligence & Power Imbalance Platform

Fine-tuned LegalBERT and Legal-RoBERTa-large ensemble classifying every clause across a 100-type unified taxonomy (CUAD, LEDGAR, MAUD - 132k clauses, macro F1 0.606). Anomaly detection fuses Isolation Forest with a 1024-dim autoencoder; bilateral power imbalance is scored −100 to +100 via sentiment, modal verb, and obligation NLP. Every prediction is explained at token level with SHAP, served through a FastAPI REST API and dark-theme dashboard with downloadable PDF reports.

AI Revenue Leakage Detection Platform

Production contract intelligence platform with XGBoost/Logistic Regression ensemble (70/30) scoring invoice leakage probability, Isolation Forest anomaly detection per record, and Prophet forecasting with 90% CI bands over a 24-month revenue horizon. SHAP attribution pipeline exposes top risk drivers per invoice; a 7-rule leakage engine generates 13k+ alerts covering missing payments, underbilling, and duplicates. Predictions served via DRF API with a Chart.js dashboard and per-invoice drill-down modals.

AI Portfolio Stress Testing Platform

Production risk platform with GMM regime classification, Ledoit-Wolf shrinkage, and XGBoost/ElasticNet trained on 50+ macro features, delivering Sharpe 4.42, VaR -12.07%, and CVaR -17.48%. Stress-tested 20+ historical crises with SHAP attribution and plain-English narratives, plus reverse stress-testing for threshold-based shock back-solving. FastAPI dashboard refreshes 20 live instruments hourly.

PolicyGate: AI Agent Action Firewall

Deployed a framework-agnostic LLM agent firewall with a YAML policy DSL (6 operators, hot-reload on file mtime), dual-layer safety enforcement via system prompt and mechanical gate, and per-response confidence scoring (High/Medium/Low) via constrained secondary LLM call. Supports LangChain, CrewAI, and AutoGen via POST /gate. All gate decisions logged to SQLite with WAL mode and threading.Lock() for concurrent write safety.

AI Lead Quality Scoring & Spam Detection

Fine-tuned BERT (99.12%) and RoBERTa (99.41%) for spam detection, paired with XGBoost scoring at R²=0.9996 across 4,137 engineered features from UK Companies House, firmographics, and DNS/SMTP signals. Deployed via FastAPI with live SHAP explanations, a Tailwind SPA, and an automated nightly retraining pipeline.

Done in collaboration with Prince Kumar Nath.