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Nikita3005/README.md

DATA SYSTEMS / APPLIED ML / TRUSTWORTHY AI

Nikita Gajbhiye

Data Science · Machine Learning · Data Engineering

I build reliable data and ML systems, with a focus on distribution shift, rigorous evaluation, and secure AI agents.

Model reliability Data engineering Agent security

Contact by email Connect on LinkedIn Explore repositories

Featured projects

🛡️ TaintGate

AI agent security Python

A runtime guard that evaluates protected AI agent tool calls before execution.

Method: Tracks input provenance, evaluates security findings, and applies deterministic policy to return ALLOW, REVIEW, or BLOCK.

Validation: A local regression suite checks expected decisions across 12 included attack scenarios. The repository also includes framework and MCP integrations.

Code and attack suite →

Model monitoring Experimental evaluation

A benchmark investigating early warnings of synthetic-data-induced model degradation.

Design: 3 datasets × 3 mechanisms × 5 seeds × 11 levels = 495 controlled conditions.

Evaluation: Compares drift signals using discrimination and warning lead time, and examines whether thresholds transfer across datasets.

Code →  ·  Technical report →

Applied ML Model serving

A modular customer ML platform covering churn prediction, lifetime value, and segmentation.

System: Feature engineering and model evaluation connect to SHAP explanations, MLflow experiment tracking, and FastAPI serving, with automated tests and CI.

Models and system design →

Data platform Databricks

A shipment data platform for operational risk analysis.

Architecture: Databricks Bronze, Silver, and Gold layers prepare shipment data for delay prediction, vendor risk analysis, and SQL dashboards.

Pipeline and analytics →

Additional work

Project Technical focus
HealthLynked Provider Pipeline Record changes · Duplicate detection · Confidence scoring · Human review
Fraud Detection & Risk Modeling Imbalanced classification · Logistic regression · Random forests · Precision–recall
Customer Churn Analytics Customer behavior · Churn prediction · Power BI reporting

Technical snapshot

Experimental evaluation Security regression Risk modeling

495

controlled conditions
DriftForge ↗

12/12

local attack checks
TaintGate ↗

284,807

transactions in dataset
Fraud Detection ↗

DATA & COMPUTE MODELING & EVALUATION SYSTEMS & ANALYTICS
Python SQL
PySpark Databricks
scikit-learn XGBoost
SHAP MLflow
FastAPI Docker
Power BI

Pinned Loading

  1. taintgate taintgate Public

    Provenance-aware runtime security for AI agents with deterministic ALLOW / REVIEW / BLOCK enforcement.

    Python 18

  2. DriftForge DriftForge Public

    Early-warning research benchmark for synthetic-data-induced model degradation using cross-dataset drift metrics, warning lead time, and statistical validation.

    Python 9

  3. enterprise-customer-intelligence-platform enterprise-customer-intelligence-platform Public

    Production-ready Machine Learning platform for customer intelligence featuring FastAPI, MLflow, SHAP explainability, model serving, and automated testing.

    Python 1

  4. Global-Supply-Chain-Risk-Intelligence-Platform Global-Supply-Chain-Risk-Intelligence-Platform Public

    Jupyter Notebook 1

  5. HealthLynked-Provider-Pipeline HealthLynked-Provider-Pipeline Public

    Python 1

  6. Fraud-Detection-Risk-Modeling Fraud-Detection-Risk-Modeling Public

    Jupyter Notebook 1