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Quick Jump → 🛡️ DataShuddi · ⚡ CodeSlim · 🤖 RetinaFace Pro · ⚖️ Bias Drift Guardian · 🧰 Stack · 📊 Stats · 🤝 Connect
class SachinPaunikar:
role = "Applied Generative AI & Agentic Systems Engineer | Senior Data Scientist"
location = "Nagpur → Open to Remote Global & India Tech Hubs (Bangalore · Pune · Hyderabad · Mumbai) 🇮🇳"
experience = "SparrowAI Research & Development Center | Sparrow AI Pvt. Ltd."
education = "B.E. in Electrical Engineering — Nagpur University (RTMNU) | CGPA: 8.2 / 10.0"
open_to = [
"Applied Generative AI Engineer",
"Agentic Systems Engineer",
"LLM Infrastructure & RAG Architect",
"Senior Data Scientist / MLOps Engineer",
"Remote Global · Relocation Ready"
]
core_pillars = [
"🛡️ Sovereign RAG Firewalls (LangGraph · Microsoft Presidio · Qdrant Vector Storage · Zero Cloud Leakage)",
"⚡ Deterministic Agentic Tooling (LibCST · Tree-Sitter · AST Guardrails · Context Minimization)",
"👁️ Computer Vision Forensics (RetinaFace · ArcFace · 5-Point Landmark Transformation · Deepfake Evidence)",
"📊 Production Model Governance (Statistical Drift · PSI · KS-Test · Fairlearn · SHAP Intersectional Auditing)",
"🏭 Full-Stack AI Deployability (Docker Compose · FastAPI · Streamlit · PostgreSQL PostgresSaver · Pytest)",
]
flagships = {
"DataShuddi": "8-Node LangGraph RAG Firewall · 100% PII Recall · 0.00% Residual Leak Rate · 66/66 Pytests",
"CodeSlim": "76% LLM Token Reduction · LibCST AST Guardrails · 0% Hallucination Corruption",
"RetinaFace": "Real-Time Face Detection & Deepfake Verification · ~45ms Latency · HuggingFace Deployed",
"BiasDrift": "Intersectional Fairness & Drift Monitoring · PSI / KS Tests · EEOC / EU AI Act Aligned"
}
philosophy = "Make AI infrastructure measurably reliable, sovereignly private, and deterministically auditable."| # | System | Architectural Role | Core Stack | Live & Artifacts |
|---|---|---|---|---|
| 🏆 | DataShuddi (3.2.0) | Enterprise RAG Security Firewall · 8-node LangGraph state machine, dual-engine Presidio + Ollama PII redaction, self-correcting reflection loop, Qdrant vector storage | Python 3.11 LangGraph Presidio Qdrant FastAPI PostgreSQL Docker | ⭐ Repo |
| ⚡ | CodeSlim | Deterministic Agentic Engine · 6-stage AST optimization engine slashing AI code bloat by 76% via LibCST + Tree-Sitter with 0% hallucination | Python LibCST Tree-Sitter LangGraph FastAPI Ollama Docker | ⭐ Repo |
| 🤖 | RetinaFace Pro | Deepfake Evidence & Biometrics · Real-time dense face localization, 5-point landmark alignment & ArcFace hyperspace verification (~45ms edge latency) | RetinaFace ArcFace OpenCV TensorFlow MLflow Docker | 🔴 Live App |
| ⚖️ | Bias Drift Guardian | Production ML Drift & Fairness · Intersectional bias detection (compound subgroups), PSI, Kolmogorov-Smirnov, SHAP & DiCE counterfactual XAI | Python Streamlit Scikit-learn Fairlearn SHAP Docker | 🔴 Live App |
Garbage In = Garbage Out. DataShuddi is the sovereign pre-ingestion firewall that neutralizes prompt injections, strips boilerplate, and redacts PII with zero offset drift before embeddings touch your vector database.
[Raw Untrusted Document (.pdf / .docx / .txt)] │ ├─ Node 1: EXTRACTOR PyMuPDF / python-docx / PaddleOCR raw parser ├─ Node 0: SECURITY SANITIZER Adversarial prompt injection & jailbreak neutralization ├─ Node 2: H/F DETECTOR Cross-page frequency density boilerplate & disclaimer stripper ├─ Node 3: PII DETECTOR Microsoft Presidio (SpaCy en_core_web_lg) + Ollama Qwen 2.5 Dual Engine ├─ Node 4: PII MASKER Interval-resolved right-to-left substitution (Zero offset drift) ├─ Node 5: SCHEMA VALIDATOR Pydantic v2 validation & compliance quality scoring │ │ │ ├─ [Validation Passed] ───> Node 7: OUTPUT NODE (Sentence Chunking -> mxbai 1024d -> Qdrant Upsert) │ │ │ ├─ [Validation Failed] ───> Node 6: REFLECTOR (Qwen 14b Diagnostic Critique Loop, conf >= 0.70) │ │ └── (Retry Loop < 3) ───> Rerun Node 1 with tuned delta │ │ │ └─ [Retries Exhausted >= 3] ──> 🚨 HUMAN REVIEW QUEUE (data/human_review/queue.jsonl) │ └─ [Sanitized Payload & Vector Ingested] ──> GDPR Article 17 Purge Ready
| Metric | Score | Production Gate | Status |
|---|---|---|---|
| PII Detection Recall | 100.0% | $\ge 95.0%$ | ✅ PASSED |
| Composite F1-Score | 90.9% | $\ge 90.0%$ | ✅ PASSED |
| Clean-Path Latency | < 150 ms / doc | $< 350 ext{ ms}$ | ✅ PASSED |
| Residual Leak Rate | 0.00% | $0.00%$ (Hard Gate) | ✅ PASSED |
| Automated Test Coverage | 66 / 66 Tests | $100%$ Green | ✅ PASSED |
The AI coding explosion created structurally bloated, hallucination-prone code. CodeSlim is the automated deterministic guardrail that sits between LLMs and your production codebase.
INPUT → .py file / directory / GitHub PR Webhook
│
├─ Stage 1 · STATIC SENSORS Radon (CC) · Vulture (dead code) · Lizard (NLOC) · Tree-Sitter AST · MD5 Duplication
├─ Stage 2 · CONTEXT MINIMIZER LibCST Lossless Pruning · TF-IDF Docstring Compression · tiktoken Budget Enforcer
├─ Stage 3 · DETERMINISTIC FIX LibCST unused-import purge · SHA-256 DiskCache · $0.00 LLM cost (80% of all fixes)
├─ Stage 4 · LLM REFACTOR CC > 10 functions only · Ollama → OpenAI → Groq → CST Fallback (privacy-first)
├─ Stage 5 · AST SAFETY GATE ast.parse() syntax check · Public API preservation · Broken LLM → CST fallback
└─ Stage 6 · OBSERVATORY UI Rich Terminal Dashboard · HTML Observatory · FastAPI Web Studio · GitHub PR Bot
- Bloat Slashed: Slashes LLM token bloat by 76% using deterministic CST pruning with 0% hallucination risk.
- $0 for 80% of Fixes: Pure C-native and AST transformations handle dead imports and scaffolding without invoking expensive LLM tokens.
Industry-grade computer vision for real-time dense face detection, 5-point landmark transformation, and ArcFace hyperspace identity verification.
- Architecture Trade-off: Feature Pyramid Network (FPN) single-pass localization over slow two-stage RPNs; MobileNet-0.25 backbone delivers ~45ms latency (22 FPS) on edge GPUs.
- Spatial 5-Point Alignment: Normalizes eye, nose, and mouth corner coordinates, preventing the ~18% embedding degradation common in unaligned verification pipelines.
- ArcFace Hyperspace Separability: Employs Additive Angular Margin Loss for compact intra-class clustering and maximized inter-class margins for deepfake and forensic verification.
- MLflow Observability: Live tracking of inference latency, confidence calibration, and landmark drift.
Detect bias before it becomes a lawsuit. Monitor drift before it breaks your model.
- Intersectional Fairness: Detects compound subgroup discrimination (e.g. Female + Age 50+ $ o$ 38% approval rate) where single-attribute tools report false passes.
- Statistical Drift Engines: Automated Population Stability Index (PSI), Kolmogorov-Smirnov, and Chi-Square distribution tests.
- Explainability: SHAP feature drift attribution + DiCE counterfactual What-If audit scenarios (EEOC & EU AI Act aligned).
Agentic AI, LLM & RAG Systems
Machine Learning & Computer Vision
Data Infrastructure & Databases
Deployment, MLOps & APIs
🏢 SparrowAI Research and Development Center | Data Scientist & AI Researcher | Aug 2025 – Jan 2026 → Built DataShuddi: 8-node LangGraph RAG security firewall with Presidio + Ollama (66/66 Pytests) → Built CodeSlim: 6-stage LangGraph AST engine slashing AI code bloat by 76% (96/96 Pytests) → Built Bias Drift Guardian: Intersectional fairness & statistical drift detection pipeline → Deployed containerized Docker Compose stacks and automated GitHub Actions CI/CD workflows 🏢 Sparrow AI Pvt. Ltd. | Data Science Intern | Jan 2025 – Jun 2025 → Customer churn prediction & sales trend forecasting (classification + regression) → Automated preprocessing and ETL pipelines — reduced manual effort by 30% → Engineered interactive analytical telemetry dashboards (Streamlit · Power BI)
🎯 Currently open to:
Applied Generative AI Engineer · Agentic Systems Engineer · LLM Infrastructure Specialist · Senior Data Scientist
🌍 Available for: Remote Global + India Tech Hubs (Bangalore · Pune · Hyderabad · Mumbai)
📬 Response time: < 24 hours
"Build AI infrastructure that is fast, sovereignly private, and deterministically auditable at every stage of the pipeline."
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