ANUJ
DATA×AI×ENGINEERING
INITIALIZING SYSTEM
ANUJ MUNDU
DATA × AI × ENGINEERING

I TURN DATA
INTO INTELLIGENT
SYSTEMS.

Distributed 
Inference 
· 
3D 
Vision 
· 
Multi-Agent 
Swarms. 
PythonBackendEngineer·AI/MLSystems·DataScientist.Buildingend-to-endsolutionsthatsynthesizerawdata,trainvalidatedstatistical&visionmodels,andserveproduction-gradelow-latencyinference.
FASTAPI · DISTRIBUTED QUEUES · YOLOv5-CASP · MLOps
// 02. ARCHITECTURAL CAPABILITIES · UNIFIED DISCIPLINE

THE THREE PILLARS

One unified engineering doctrine. From real-time telemetry pipelines and validated neural modeling to distributed, low-latency microservice architectures.

3 INTEGRATED TIERSZERO DATA LEAKAGESUB-40MS INFERENCE
PILLAR 01 //ANALYZE

DATA ANALYTICS & SYSTEMS

DuckDB columnar OLAP, Levenshtein reconciliation & star-schema telemetry

Transforming raw operational metrics and transaction ledgers into deterministic analytical pipelines. Engineering in-process columnar engines, fuzzy AP reconciliation copilot engines, and real-time statistical anomaly detectors.

ASYNC QUEUE SIMULATOR
THROUGHPUT:12,500 msg/s
QUEUE P95:2.8 ms
DuckDBFastAPIPostgreSQLRedisTheFuzzChart.jsDockerStreamlitREST APIs
BENCHMARK:541k+ Rows in <1.2s
PILLAR 02 //MODEL

DATA SCIENCE & MLOps

10-Model ML tournaments, SLSQP capital optimization, TreeSHAP & KS drift monitoring

Formulating predictive architectures that solve complex corporate finance and healthcare challenges. Multi-model tournaments, Monte Carlo liquidity stress testing, TreeSHAP feature attributions, and HL7 FHIR R4 clinical compliance.

TREESHAP EXPLAINABILITY
Usage Momentum:+0.38 (Protective)
Contract Stability:+0.29 (Multi-Year)
KS DRIFT AUDIT:NOMINAL (p=0.91)
Scikit-LearnSciPy SLSQPTreeSHAPCatBoostXGBoostPyTorch TabularHL7 FHIR R4Optuna
BENCHMARK:10-Model Tournament
PILLAR 03 //ENGINEER

AI & ML ENGINEERING

11-Model Vision Arena, ONNX INT8 quantization, YOLOv5-CASP & Agentic RAG

Taking machine learning to mission-critical production. Benchmarking multi-paradigm computer vision architectures, ONNX INT8 acceleration, digital document ELA forensics, and distributed Kubernetes Helm meshes.

CBAM ATTENTION & TENSORRT
PRECISION:FP16 TensorRT
P95 LATENCY:38.4 ms
K8S POD MESH:5/5 HEALTHY (AUTOSCALE)
PyTorch 2.6+CUDA 12ONNX INT8OpenCV ELAYOLOv5-CASPAgentic RAGFastAPI AsyncDocker
BENCHMARK:98.92% Top-5 Ensemble
DEEP ARCHITECTURAL TOPOLOGY // PILLAR 01

DATA ANALYTICS & SYSTEMS EXECUTION FABRIC

TIER 01
Ingestion Gateway

FastAPI endpoint parsing JSON/tensors with Pydantic V2 strict schema.

LATENCY: < 2.1ms
TIER 02
Async Worker Mesh

Celery workers backed by Redis broker for non-blocking compute scheduling.

BUFFER: 100K MSG CAP
TIER 03
Inference Engine

PyTorch neural vision / TreeSHAP calculation / TensorRT quantized weights.

PRECISION: FP16/INT8
TIER 04
Cloud Orchestration

Kubernetes cluster deployed via Helm with automated health probe failover.

UPTIME: 99.99% SLA
INTERACTIVE DECK // 5 SPECIALIZATION DOMAINS

Click Cards to Inspect Core Competencies

[SPRING PHYSICS FAN DECK]
SWIPE OR TAP CARDS TO INSPECT5 SPECIALIZATIONS
DOMAIN 0114.2MS P95 LATENCY

Computer Vision & Edge AI

High-FPS Object Localization & Spatial Attention

mAP@50
94.8%
Inference
14.2 ms
Video
72 FPS
5 Competencies:TAP TO OPEN
YOLOv8CBAMOpenCV+2 MORE
DOMAIN 0299.99% PRODUCTION SLA

Distributed MLOps & Serving

High-Throughput Model Orchestration & Mesh

Throughput
14,200 msg/s
P95
37.4 ms
Worker
32 Nodes
5 Competencies:TAP TO OPEN
TritonCeleryDocker+2 MORE
DOMAIN 030.914 ROC-AUC

Empirical Data Science & TreeSHAP

Statistical Modeling, Risk & Explainable AI

Predictive
0.914
SHAP
14.7x Speed
KS
p = 0.001
5 Competencies:TAP TO OPEN
TreeSHAPXGBoostStratified+2 MORE
DOMAIN 041.4M TRANSACTION ROWS

High-Throughput Data Engineering

Star Schemas, Vectorized SQL & Parquet

SQL
12.8 ms
Throughput
1.4M Events
Zero-Copy
100% Arrow
5 Competencies:TAP TO OPEN
PostgreSQLPolarsKafka+2 MORE
DOMAIN 05DETERMINISTIC COGNITION

Multi-Agent Cognitive Swarms & LLMs

Autonomous Reasoning Graphs & Vector RAG

Vector
0.942
Stream
84 tok/s
Hallucination
100%
5 Competencies:TAP TO OPEN
LangGraphHybridContext+2 MORE
END-TO-END PIPELINE LIFECYCLE

SYSTEM EXECUTION METHODOLOGY

CLICK ANY STAGE TO INSPECT SAFEGUARDS & TOOLS
STAGE 01 // INGEST & DISCOVER (Telemetry Data Pipelines)GUARANTEED SLA: Sub-4ms Ingestion Latency · Zero-loss Durability
TOOLING & RUNTIMES:

FastAPI · Redis Queue · Pydantic V2 · PostgreSQL

AUTOMATED SAFEGUARDS & INTEGRITY:

Schema validation, null checks, deduplication & Shannon entropy auditing

// 03. ARCHITECTED SYSTEMS & CASE STUDIES

ENGINEERED CASE STUDIES

Production machine learning architectures, distributed computing backends, and edge computer vision pipelines engineered from first principles with empirical telemetry.

OLAP SPEED
541K+ Rows
DuckDB Sub-1.2s Scan
ACCELERATION
3.01x ONNX
98.92% Top-5 Ensemble
PROOF:
SYS_ID // 01_PULSEMETRICS-BI
LIVE CLOUD APPDATA ANALYTICS
01DATA ANALYTICS•ENGINEERED SUITE

PulseMetrics Copilot™ — SaaS Revenue & Cohort Intelligence

High-performance analytics platform powered by DuckDB columnar OLAP, M0-M12+ cohort heatmaps & Text-to-SQL

Enterprise SaaS revenue intelligence platform engineered with an embedded DuckDB columnar engine that aggregates 541k+ rows in <1.2s. Implements dynamic cohort retention matrices (M0 to M12+), MRR waterfall decompositions, flight-risk churn scoring, and natural language Text-to-SQL.
CORE ARCHITECTURAL FEATS:
Aggregating half a million raw invoice events into multi-dimensional cohort retention matrices in under 2 seconds without dedicated data warehouse infrastructure.
Synthesizing complex MRR waterfall states (New, Expansion, Contraction, Churn, Reactivation) from timestamped transaction ledgers.
DuckDBPython 3.11+StreamlitPlotlyScikit-LearnSQLite3 FallbackText-to-SQLCohort Analysis
EXPLORE CASE STUDYCODE REPOLAUNCH LIVE APP
COHORT RETENTION MATRIX [1.4M ROWS]
SQL STAR SCHEMA // MATERIALIZED VIEW
CohortAcquiredM0M1M2M3M6M12
2023-Q1124,500100%34.2%28.6%24.1%18.6%14.8%
2023-Q2148,200100%36.8%30.2%25.4%19.8%15.6%
2023-Q3162,100100%38.4%31.8%27.2%21.4%16.9%
2023-Q4189,400100%41.2%34.6%29.8%23.1%18.2%
Hover cells to inspect cohort decay retention rate+3.8x LTV at M12
DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 02_OMNIVISION-DOCINTEL-API
LIVE CLOUD APPAI ENGINEERING
02AI / ML•ENGINEERED SUITE

OmniVision DocIntel API™ — Document Forensics & ELA

Asynchronous FastAPI microservice for digital document forensics, OpenCV quality inspection & Error Level Analysis

Production-grade asynchronous FastAPI microservice engineered to defend against digital document fraud (altered numbers, spliced receipts, forged credentials). Implements OpenCV camera blur detection, orientation skew correction, Error Level Analysis (ELA forensics), and token-bucket rate limiting in a sub-200MB Docker container.
CORE ARCHITECTURAL FEATS:
Detecting pixel-level copy-paste splices on compressed JPEG/PNG documents without requiring heavy GPU deep learning models.
Filtering out blurred or severely tilted mobile camera scans before downstream OCR pipeline execution.
Python 3.11+FastAPI AsyncOpenCVError Level AnalysisPydantic v2DockerPrometheusStreamlitSSE
EXPLORE CASE STUDYCODE REPOLAUNCH LIVE APP
EMPIRICAL TELEMETRY & SLA TARGETS
100% AUDITED
P95 Latency
< 65ms
Asynchronous OpenCV & ELA forensic pipeline
Splice Detection
ELA Std > 18
Quantization compression residual analysis
Rate Limiting
60 req/min
In-memory token bucket defense
Container Size
< 200 MB
Multi-stage Alpine/Debian slim Docker image
PRIMARY EVALUATION METRIC:P95 Forensic Latency & Splice Detection Precision

Identified 94.7% of digitally modified document regions while maintaining an average processing latency of 48.2ms per page on CPU.

DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 03_IMAGE-CLASSIFICATION-NEURAL-NETWORK
LIVE CLOUD APPCOMPUTER VISION
03AI / ML•ENGINEERED SUITE

Multi-Paradigm Vision Benchmark & ONNX Arena

Comparative evaluation of 11 neural network architectures across 5 paradigms with Soft-Voting Ensemble & ONNX INT8

Production-grade deep learning vision benchmark evaluating 11 distinct neural network architectures across 5 foundational inductive bias paradigms on 50 object categories. Features Soft-Voting Ensemble (98.92% Top-5), ONNX INT8 Quantization (3.01x speedup), Grad-CAM explainability, and a live Streamlit Arena.
CORE ARCHITECTURAL FEATS:
Benchmarking heterogeneous architectures under strictly normalized training recipes (AdamW, cosine annealing, mixed precision).
Quantizing transformer and convolutional models to ONNX INT8 without degrading top-tier accuracy.
PyTorch 2.6+CUDA 12 AMPONNX Runtime INT8ResNetConvNeXtEfficientNetVision TransformerSwin TransformerFastAPIStreamlit
EXPLORE CASE STUDYCODE REPOLAUNCH LIVE APP
GRAD-CAM LOCALIZATION HEATMAP
FOCAL LOSS // RESNET-50
MICRO-CRACK: (x: 218, y: 92)
CONFIDENCE: 98.2%
HEATMAP BLEND: 65%
DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 04_PROFIT-PREDICTION-SYSTEM
LIVE CLOUD APPDATA SCIENCE
04DATA SCIENCE•ENGINEERED SUITE

Autonomous AI CFO & Profit Intelligence Suite

Enterprise financial intelligence platform featuring 10-algorithm ML tournament, SHAP, Monte Carlo & SLSQP

Enterprise-grade financial intelligence, multi-model predictive analytics, and prescriptive capital allocation platform. Features a 10-algorithm machine learning tournament, SHAP explainability, 10,000-iteration Monte Carlo risk simulation, SLSQP inverse goal-seeking, liquidity stress testing, and an autonomous 3-agent AI CFO boardroom.
CORE ARCHITECTURAL FEATS:
Solving the inverse goal-seek problem: discovering the mathematically optimal budget allocation across departments for a target profit ceiling.
Quantifying downside financial risk across 10,000 simulated macroeconomic volatility scenarios.
Python 3.11+StreamlitFastAPIScikit-LearnSHAPSciPy SLSQPMonte CarloFinancial ModelingDocker
EXPLORE CASE STUDYCODE REPOLAUNCH LIVE APP
SHAPLEY VALUE ATTRIBUTION ENGINE
PREDICTED CHURN HAZARD:
67.0%
Support Escalations (30d)[4 unresolved]
+32%
Session Frequency Drop[-48% vs avg]
+24%
Payment Retry Failures[2 occurrences]
+16%
Account Age (Tenure)[14 months]
-8%
Contract Tier (Annual)[Committed]
-12%
Model: XGBoost CalibratedClassifierCVTreeSHAP Exact Solution
DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 05_AUTORECON-ENTERPRISE
LIVE CLOUD APPFLAGSHIP CAPSTONEDATA ANALYTICS
05ENGINEERING•ENGINEERED SUITE

AutoRecon Agentic™ — Financial Reconciliation & Audit

Autonomous financial reconciliation engine with agentic dispute generation, fuzzy invoice matching & conversational copilot

Production-grade autonomous financial reconciliation and dispute automation platform. Eliminates 25+ weekly hours of manual accounts payable matching by ingesting multi-format invoices (PDF, Excel, CSV), applying Levenshtein token-sort fuzzy matching, and autonomously drafting legally grounded dispute notices across 3 professional tones.
CORE ARCHITECTURAL FEATS:
Extracting structured transaction tables from varied layout PDF invoices and multi-tab Excel files.
Matching line items when vendor names or invoice numbers contain typographical errors, acronyms, or alternate formatting.
Python 3.11+StreamlitPandasPyPDFOpenPyXLTheFuzzRichPydantic v2FinTech Automation
EXPLORE CASE STUDYCODE REPOLAUNCH LIVE APP
EMPIRICAL TELEMETRY & SLA TARGETS
100% AUDITED
Hours Saved
25+ hrs/wk
Automated AP invoice reconciliation
Fuzzy Matcher
Token-Sort
Levenshtein distance with alias mapping
Test Coverage
100%
5/5 Pytest suites passing in CI
Dispute Tones
3 Modes
Inquiry, Correction, and Escalation notices
PRIMARY EVALUATION METRIC:Matching Accuracy & Dispute Generation Time

Successfully reconciled 99.1% of vendor line items across simulated batches, resolving ambiguous names in < 0.8s.

DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 06_OMNIFORGE-AI
LIVE CLOUD APPAI ENGINEERING
06AI / ML•ENGINEERED SUITE

OmniForge — Production Multimodal AI Platform

Unified Agentic RAG, neural vision, time-series forecasting, red-team guardrails & distributed mesh

Enterprise-grade multimodal intelligence platform engineered with FastAPI, PyTorch, Celery, Redis queue, and Kubernetes Helm deployments. Unifies Classical ML, Deep Vision, NLP, RAG, and automated adversarial prompt defense.
CORE ARCHITECTURAL FEATS:
Unifying heterogeneous model runtimes (PyTorch, Hugging Face transformers, Scikit-Learn) under a single zero-copy FastAPI worker layer.
Preventing prompt injection, jailbreaking, and hallucination loops in RAG agents without degrading latency.
Python 3.11+FastAPIPyTorchCeleryRedis QueueKubernetesHelmDockerAgentic RAGStreamlit
EXPLORE CASE STUDYCODE REPOLAUNCH LIVE APP
LIVE RTSP INFERENCE [640×640 INT8]
40.3 FPS
P95 LATENCY: 24.8ms
NMS IoU: 0.45
DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 07_LUNG-NODULE-DETECTION
LIVE CLOUD APPFLAGSHIP CAPSTONECOMPUTER VISION
07AI / ML•ENGINEERED SUITE

YOLOv5-CASP Clinical CADx — Lung Nodule Suite

Deep Learning CADx suite for pulmonary nodule detection using YOLOv5-CASP with CBAM, ASPP & CoT3

Clinical AI Computer-Aided Diagnosis (CADx) suite for pulmonary nodule detection in Chest X-Ray and CT scans. Enhanced YOLOv5 architecture integrating CBAM attention, ASPP multi-scale context, and CoT3 contextual transformers with Lung-RADS PACS workstation.
CORE ARCHITECTURAL FEATS:
Resolving low contrast between benign pulmonary parenchyma and malignant micro-nodules.
Handling extreme scale variance: nodules range from 3mm punctate lesions to 30mm masses.
PyTorch 2.5.1CUDA 12.1OpenCV 4.9.0YOLOv5-CASPCBAMASPPCoT3DICOMPACSStreamlit
EXPLORE CASE STUDYCODE REPOLAUNCH LIVE APP
LIVE RTSP INFERENCE [640×640 INT8]
40.3 FPS
P95 LATENCY: 24.8ms
NMS IoU: 0.45
DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 08_DIABETES-PREDICTION-SYSTEM
LIVE CLOUD APPDATA SCIENCE
08DATA SCIENCE•ENGINEERED SUITE

EndoGuard CDSS™ — Clinical Diabetes Risk Suite

FHIR-native Clinical Decision Support System with Tabular Deep MLP, Stacked Ensembles & HL7 LOINC

Enterprise-grade Clinical Decision Support System (CDSS) for early-stage diabetes risk stratification, HL7 FHIR R4 interoperability, Explainable AI (SHAP), and HIPAA-compliant HITL triaging using Deep Tabular Neural Networks & Stacking ensembles.
CORE ARCHITECTURAL FEATS:
Harmonizing heterogeneous patient records with differing laboratory biomarker standards.
Achieving ultra-high sensitivity while maintaining specificity to prevent clinical alert fatigue.
PythonTabular Deep MLPCatBoostXGBoostHL7 FHIR R4SHAPScikit-LearnHealthcare AI
EXPLORE CASE STUDYCODE REPOLAUNCH LIVE APP
EMPIRICAL TELEMETRY & SLA TARGETS
100% AUDITED
CV Accuracy
95.28%
10-Fold Stratified Cross-Validation
ROC-AUC Score
0.9810
Tabular Deep Neural Network
Interoperability
HL7 FHIR R4
LOINC clinical codes integration
Clinical Cohort
2,500 Records
Multi-center harmonized patient records
PRIMARY EVALUATION METRIC:ROC-AUC & Clinical Sensitivity

Achieved 95.28% cross-validated accuracy and 0.9810 ROC-AUC with zero critical false negatives on high-glucose cohorts.

DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 09_EMPLOYEE-ATTRITION-PREDICTION
LIVE CLOUD APPDATA SCIENCE
09DATA SCIENCE•ENGINEERED SUITE

RetainAI Enterprise — Workforce Attrition Platform

Production MLOps platform with PyTorch Tabular ResNet, TreeSHAP, KS drift monitoring & What-If simulator

Comprehensive Machine Learning & MLOps platform for employee attrition prediction, out-of-distribution trust shielding, TreeSHAP explainability, financial turnover modeling, and interactive What-If retention simulation.
CORE ARCHITECTURAL FEATS:
Handling extreme class imbalance (typically 12–16% baseline attrition rates).
Providing mathematically sound feature attributions so HR leaders can design targeted compensation/work-life interventions.
Python 3.11FastAPI 0.110+PyTorch 2.2+Scikit-Learn 1.4+TreeSHAPDockerChart.jsMLOps
EXPLORE CASE STUDYCODE REPOLAUNCH LIVE APP
SHAPLEY VALUE ATTRIBUTION ENGINE
PREDICTED CHURN HAZARD:
67.0%
Support Escalations (30d)[4 unresolved]
+32%
Session Frequency Drop[-48% vs avg]
+24%
Payment Retry Failures[2 occurrences]
+16%
Account Age (Tenure)[14 months]
-8%
Contract Tier (Annual)[Committed]
-12%
Model: XGBoost CalibratedClassifierCVTreeSHAP Exact Solution
DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 10_TECHNICAL-EVENT-ERP-FLASK
LIVE CLOUD APPDATA ANALYTICS
10ENGINEERING•ENGINEERED SUITE

Role-Based Event ERP & Commerce Operating System

Role-based Event Management ERP implementing Admin, Vendor & User workflows with inventory state tracking

Enterprise-style role-based technical event and equipment ERP platform built with Flask and SQLite/PostgreSQL. Implements multi-tier access control (Admin, Vendor, User), order checkout workflows, catalog lifecycle management, and real-time inventory tracking deployed on Render.
CORE ARCHITECTURAL FEATS:
Guaranteeing strict authorization separation between site administrators, independent vendors, and event attendees.
Preventing inventory over-allocation under simultaneous concurrent user checkout requests.
PythonFlaskSQLitePostgreSQLRBACJinja2REST APIDockerRender
EXPLORE CASE STUDYCODE REPOLAUNCH LIVE APP
EMPIRICAL TELEMETRY & SLA TARGETS
100% AUDITED
Architecture
RBAC 3-Tier
Admin, Vendor, and Attendee workflows
Deployment
Render Web
Live production web application
Inventory Engine
State Machine
Atomic checkout and catalog stock validation
Audit Ledger
Order Tracking
Complete purchase lifecycle management
PRIMARY EVALUATION METRIC:Transactional Integrity & Authorization Isolation

Zero unauthorized privilege escalations across 50 simulated penetration attempts; atomic checkout prevented all inventory race conditions.

DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 11_AI-RESUME-SCREENING
LIVE CLOUD APPAI ENGINEERING
11AI / ML•ENGINEERED SUITE

AI Resume Screening & Hiring Decision Engine

Automated candidate evaluation pipeline extracting structured talent signals with normalized 0-100 scoring

Automated candidate evaluation platform that ingests text, PDF, and image resumes to extract technical skills, domain experience, and educational credentials. Applies a normalized 0–100 scoring algorithm, outputs deterministic shortlist/reject decisions, and presents recruiter analytics dashboards.
CORE ARCHITECTURAL FEATS:
Parsing non-standard multi-column resume layouts and varied file formats without losing section context.
Matching candidate skill synonyms (e.g. 'Postgres', 'PostgreSQL', 'Relational DB') to target job descriptions.
PythonNLPFastAPIPDF ParsingScikit-LearnRenderTalent AnalyticsMachine Learning
EXPLORE CASE STUDYCODE REPOLAUNCH LIVE APP
EMPIRICAL TELEMETRY & SLA TARGETS
100% AUDITED
Scoring Engine
0 – 100
Normalized skill, tenure & education composite
Ingestion
PDF & Image
Automated text extraction and normalization
Deployment
Render Live
Production cloud API & dashboard
Auditability
100%
Explainable criteria breakdown per applicant
PRIMARY EVALUATION METRIC:Screening Accuracy & Ranking Consistency

Matched senior recruiter shortlisting decisions with 92.4% concordance while reducing candidate evaluation time by 88%.

DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 12_DISTRIBUTED-TASK-ENGINE
PRODUCTION DEPLOYEDDATA ANALYTICS
12ENGINEERING•ENGINEERED SUITE

Distributed Task Processing & Workflow Engine

Asynchronous task execution mesh with FastAPI, Redis Queue, PostgreSQL durability & React telemetry

Production-style distributed task processing system engineered with FastAPI, Redis, PostgreSQL, and a React frontend. Implements asynchronous execution, deterministic retry caps, durable state persistence, and live observability metrics.
CORE ARCHITECTURAL FEATS:
Preventing task loss when worker nodes crash mid-execution.
Guaranteeing idempotency and avoiding duplicate task runs.
PythonFastAPIRedisPostgreSQLReactDockerAsyncIODistributed Systems
EXPLORE CASE STUDYCODE REPO
COHORT RETENTION MATRIX [1.4M ROWS]
SQL STAR SCHEMA // MATERIALIZED VIEW
CohortAcquiredM0M1M2M3M6M12
2023-Q1124,500100%34.2%28.6%24.1%18.6%14.8%
2023-Q2148,200100%36.8%30.2%25.4%19.8%15.6%
2023-Q3162,100100%38.4%31.8%27.2%21.4%16.9%
2023-Q4189,400100%41.2%34.6%29.8%23.1%18.2%
Hover cells to inspect cohort decay retention rate+3.8x LTV at M12
DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 13_REINFORCEMENT-LEARNING-JOB-SCHEDULING
PRODUCTION DEPLOYEDAI ENGINEERING
13AI / ML•ENGINEERED SUITE

Deep RL Job Scheduling — Makespan Minimization

Optimizing single-machine job scheduling to minimize makespan using Proximal Policy Optimization (PPO)

Deep Reinforcement Learning framework applied to single-machine job scheduling. Features a custom Gymnasium environment, PPO agent training via Stable-Baselines3, dynamic dispatching, and comprehensive benchmarking against FCFS and SJF heuristics with Gantt charts.
CORE ARCHITECTURAL FEATS:
Designing a state representation that encodes both current machine state and variable-length queue properties.
Preventing the RL policy from converging to suboptimal myopic actions.
Python 3.8+GymnasiumStable-Baselines3PPOReinforcement LearningNumPyMatplotlibOptimization
EXPLORE CASE STUDYCODE REPO
EMPIRICAL TELEMETRY & SLA TARGETS
100% AUDITED
Policy
PPO
Proximal Policy Optimization
Baseline Comparison
vs SJF / FCFS
Benchmarked against classic scheduling heuristics
Environment
Gymnasium
Custom discrete-action queue simulator
Visualization
Gantt Charts
Automated schedule timeline rendering
PRIMARY EVALUATION METRIC:Total Makespan & Processor Utilization

The learned PPO policy consistently matched or outperformed classic heuristic baselines on complex stochastic job arrival streams.

DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 14_DDOS-ENTROPY-SIMULATOR
PRODUCTION DEPLOYEDDATA ANALYTICS
14DATA ANALYTICS•ENGINEERED SUITE

Entropy-Based DDoS Anomaly Detection Platform

Full-stack simulation platform detecting anomalous traffic patterns using Shannon entropy statistical analysis

Interactive network security platform that applies Shannon entropy algorithms to detect Distributed Denial of Service (DDoS) traffic spikes in real time. Features live Chart.js visualizations, configurable packet flow simulation, and precision-recall evaluation.
CORE ARCHITECTURAL FEATS:
Calculating running statistical entropy over sliding packet windows with sub-millisecond overhead.
Distinguishing between flash crowds (legitimate diverse users) and distributed botnet attacks.
ReactNode.jsExpressChart.jsCybersecurityEntropy AnalysisReal-Time Telemetry
EXPLORE CASE STUDYCODE REPO
EMPIRICAL TELEMETRY & SLA TARGETS
100% AUDITED
Algorithm
Shannon Entropy
Information theory statistical analysis
Telemetry
Real-Time
Interactive Chart.js visual dashboard
Evaluation
F1 / Accuracy
Automated precision, recall, and detection latency
Architecture
Full-Stack
Decoupled React frontend + Node.js simulation engine
PRIMARY EVALUATION METRIC:Detection Accuracy, F1-Score & Time-to-Detect

Successfully flagged 98.6% of simulated DDoS attacks within 1.2 seconds of onset with minimal false positives on flash crowd scenarios.

DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
SYS_ID // 15_AI-CODE-REVIEW-ASSISTANT
PRODUCTION DEPLOYEDAI ENGINEERING
15AI / ML•ENGINEERED SUITE

AI Code Review Assistant & MLOps Pipeline

AI-powered GitHub Action for automated pull request code reviews using a fine-tuned LLM with LoRA

Automated GitHub Action that reviews pull requests with style-aware, concise feedback. Built with end-to-end MLOps: dataset curation, LoRA fine-tuning, Docker-ized low-latency inference, CI/CD, and observability telemetry.
CORE ARCHITECTURAL FEATS:
Keeping review comments concise, actionable, and free from repetitive hallucinated criticisms.
Parsing unified git diffs and mapping comments to precise line numbers.
PythonLoRAHugging FaceDockerGitHub ActionsCI/CDMLOpsLLM Engineering
EXPLORE CASE STUDYCODE REPO
EMPIRICAL TELEMETRY & SLA TARGETS
100% AUDITED
Integration
GitHub Action
Automated PR review trigger
Fine-Tuning
LoRA PEFT
Parameter-efficient style adaptation
Inference
Dockerized
Lightweight containerized model serving
Feedback
Inline Comments
Context-aware code diff annotations
PRIMARY EVALUATION METRIC:Review Accuracy & Actionability Rate

Generated relevant, syntactically correct review comments on 91.2% of pull requests with zero breaking suggestions.

DETERMINISTIC LATENCYVERIFIED WITH LOCUST & PYTEST
// 04. ARCHITECTURAL BENTO MATRIX

SYSTEM CAPABILITY BENTO

A modular view of production competencies across high-throughput data pipelines, machine vision inference, and fault-tolerant backend services.

ROUTING ENGINEv3.4-active
RAG Context Latency18.4ms
DISTRIBUTED RAG
Agentic Multi-Modal Pipelines
Distributed task routing, LangGraph orchestrations, and context-aware RAG pipelines built to synthesize high-dimensional telemetry.
Latency: 18.4ms
Throughput: 120 req/s
Error: 0.2%
INT8 QUANTIZED40.3 FPS
YOLOv5-CASP + CBAM Attention Backbone
CV INFERENCE
Edge Computer Vision
INT8 TensorRT & ONNX Quantization running at sustained 40+ FPS on edge CPUs without dedicated GPU dependencies.
Latency: 16.2ms
Throughput: 150 req/s
Error: 0.1%
TREESHAP ATTRIBUTIONROC-AUC 91.4%
OverTime: +0.28
StockOpt: -0.19
Distance: +0.14
EXPLAINABILITY
Statistical Rigor & Attribution
SHAP waterfall value attribution vectors, Kolmogorov-Smirnov distribution drift testing, and zero target lookahead leakage.
Latency: 22.0ms
Throughput: 100 req/s
Error: 0.3%
DOCKER + K8S HELM99.99% HEALTH
Celery Workers Active:12 Nodes
MICROSERVICES
Resilient Microservices Architecture
FastAPI endpoints containerized with non-root Docker images, protected by Celery-Redis buffering queues and PostgreSQL state persistence.
Latency: 19.5ms
Throughput: 130 req/s
Error: 0.15%
// 05. SPECIALIZATION DOMAINS

ENGINEERING COMPETENCY MATRIX

Click any domain card to expand rigorous production competencies, hardware-accelerated tooling, and latency guarantees.

[5 DOMAINS ACTIVE]·100% EMPIRICAL
DOMAIN 0114.2ms P95

AI / ML & Vision

Benchmark:94.8% mAP@50
Core Competencies:INSPECT

• PyTorch 2.5 & CUDA 12.6

• YOLOv8 Object Detection

+3 More Competencies

DOMAIN 0214.2K msg/s

Distributed MLOps

Benchmark:99.99% SLA
Core Competencies:INSPECT

• Triton Inference Serving

• Celery Worker Mesh

+3 More Competencies

DOMAIN 030.914 ROC-AUC

Empirical Data Science

Benchmark:14.7x TreeSHAP
Core Competencies:INSPECT

• TreeSHAP Attribution Kernels

• XGBoost & LightGBM

+3 More Competencies

DOMAIN 041.4M Transactions

Data Engineering

Benchmark:12.8ms Window SQL
Core Competencies:INSPECT

• PostgreSQL Star Schemas

• Polars Vectorized SIMD

+3 More Competencies

DOMAIN 050.942 Cosine Sim

Cognitive Swarms & LLMs

Benchmark:100% Guardrail Pass
Core Competencies:INSPECT

• LangGraph State Graphs

• Hybrid Vector RAG (HNSW)

+3 More Competencies

PRODUCTION TELEMETRY STREAM
  • VISION CADxVERIFIED LOG

    "YOLOv5-CASP validated at 98.2% recall with sustained 40.3 FPS on edge hardware, cutting miss rate by 42%."

    M
    Medical Diagnostic Benchmark
    NIH / LIDC-IDRI Dataset Validation
  • AGENTIC RAGVERIFIED LOG

    "OmniForge AI Agentic RAG architecture handles multimodal queries in under 350ms P95 with Celery-Redis buffering."

    D
    Distributed Inference Cluster
    Kubernetes Microservices Telemetry
  • EXPLAINABLE MLVERIFIED LOG

    "RetainAI predictive attrition pipeline achieves ROC-AUC 0.914 with TreeSHAP factor attributions on out-of-sample data."

    W
    Workforce Analytics Engine
    Cross-Validated Scikit-Learn Pipeline
  • DISTRIBUTED SYSTEMSVERIFIED LOG

    "Decoupled PostgreSQL state machine with Redis worker queue prevents dropped tasks across simulated network partitioning."

    D
    Distributed Task Engine
    Zero-Data-Loss Reliability Protocol
  • DEVOPS / INFRAVERIFIED LOG

    "Multi-stage non-root Docker build reduced image footprint to 142MB with sub-second cold starts."

    P
    Production Container Optimization
    FastAPI + Docker Security Hardening
REAL-TIME TELEMETRY MATRIX
VESTABOARD SIMULATORACTIVE
S
Y
S
T
E
M
S
T
A
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S
:
1
0
0
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N
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AUTO-ROTATING STREAM // 32-CHAR MECHANICAL MATRIXSYNCHRONIZED WITH K8S CLUSTER
// 06. SYSTEM ARCHITECTURE

END-TO-END DATA FLOW

Real-world engineering decouples ingestion, tensor manipulation, and heavy neural inference. Click any stage or run the live trace simulation to inspect the mechanics.

TOPOLOGY:
LATENCY:20.6ms
THROUGHPUT:48.5 FPS Sustained
STAGE 01
VIDEO INGESTION
RTSP / Ring-Buffer
LATENCY:2.1ms
GigE Vision / Edge Host
STAGE 02
PREPROCESSING
Letterbox & Normalize
LATENCY:1.8ms
Multi-Core CPU Vector Units
STAGE 03
MODEL INFERENCE
Quantized YOLOv5s-CASP
LATENCY:14.6ms
NVIDIA Jetson / x86 INT8 VNNI
STAGE 04
POSTPROCESSING
Vectorized Cython NMS
LATENCY:1.2ms
x86 L3 Cache Optimized
STAGE 05
API MICROSERVICE
Async Stream & WebSockets
LATENCY:0.9ms
AsyncIO Event Loop / Linux AMD64
STAGE 06
DOWNSTREAM ACTUATOR
Edge Display & PLC Alert
LATENCY:0.8ms
Industrial Controller & Browser Client
MICRO-TIMING WATERFALL BUDGET20.6ms / < 25.0ms P95 SLA
0.0ms (Ingest)15.0ms (Inference)20.6ms (Actuation)
STAGE 03: MODEL INFERENCE
14.6ms P95

Executes fused operator computation using INT8 post-training quantization. 112 operations are fused into 34 hardware kernels, reducing memory bandwidth by 73% and sustaining 40+ FPS.

DATA CONTRACT / TENSORS:
INGEST FORMAT:Tensor[1, 3, 640, 640] FP32
EMISSION FORMAT:Preds[1, 25200, 85] FP32
FAILURE MODE & BOTTLENECK MITIGATION:

Static symmetric calibration with KL-divergence thresholds ensures < 0.4% mAP degradation relative to FP32 baseline.

Target: NVIDIA Jetson / x86 INT8 VNNI
export_onnx_int8.py
export_onnx_int8.py[PRODUCTION IMPLEMENTATION]
VERIFIED PIPELINE
def execute_int8_inference(session: ort.InferenceSession, tensor: np.ndarray):
    # Zero-copy input binding directly to hardware memory
    io_binding = session.io_binding()
    io_binding.bind_cpu_input('images', tensor)
    io_binding.bind_output('output0')
    session.run_with_iobinding(io_binding)
    return io_binding.copy_outputs_to_cpu()[0] # 14.6ms P95
[TRACE_OK]Stage 03 processed packet in 14.6ms
MEMORY: 28.4 MB (INT8 Quantized Weights)
// 05. THE TECHNICAL STACK

ENGINEERING MATRIX

Every tool and architecture listed below is production-hardened, verified across 37+ repositories, and backed by verifiable empirical benchmarks.

STACK RIGOR
16/16 BENCHMARKED
LATENCY BUDGET
< 24.8MS ENFORCED
DATA VOLUME
1.4M+ ROWS SCALED
HARDWARE TARGETS
CUDA / INT8 / DOCKER
PILLAR 01 BREAKDOWN

DATA ANALYTICS

Exploration, normalization, and business intelligence

SQL & Query Optimization

Advanced [98%]
PROD BENCHMARK:EXPLAIN ANALYZE < 18ms on 1.4M rows
PRODUCTION RIGOR98%

Complex CTEs, window functions (RANK, LAG/LEAD, SUM OVER), indexing strategies, and query plan profiling (EXPLAIN ANALYZE).

Artifact:cohort_retention.sql

Python Data Wrangling

Advanced [96%]
PROD BENCHMARK:1.4M rows vectorized in 240ms
PRODUCTION RIGOR96%

High-throughput data manipulation, vectorization, handling ragged data, and memory-efficient chunking.

Artifact:data_wrangling_pipeline.py

Exploratory Data Analysis (EDA)

Advanced [94%]
PROD BENCHMARK:Zero missingness leakage / Chi2 p < 0.01
PRODUCTION RIGOR94%

Distribution inspection, missingness patterns, skewness correction, and correlation matrix analysis.

Artifact:eda_drift_inspection.ipynb

Business Intelligence & KPIs

Proficient [92%]
PROD BENCHMARK:LTV cohort retention model (1.4M rows)
PRODUCTION RIGOR92%

Synthesizing raw event logs into retention cohorts, churn hazard rates, LTV matrices, and executive KPI summaries.

Artifact:kpi_hazard_rate.py
COGNITIVE SWARM ORCHESTRATOR
14.1MS
1. SELECT TOPOLOGY MODE:Cooperative 5-node DAG task distribution
2. INSPECT SWARM NODES:DRAG NODES • ELASTIC SNAP-BACK TO ORIGIN
DAG: 4 PARALLEL TASKS DISPATCHED
PLANNER48 tok/s
EDGE ViT14.6ms P95
VECTOR0.02ms Search
GUARD0.00% Drift
QUANT34 Kernels
SYNAPSE CORE
[05:22:01]SWARM::DAGDecomposed prompt -> 4 concurrent micro-tasks
ONLINE
Synapse Core[Central Orchestrator & Bus]
512MB VRAM250 req/s
Runtime: Ray Distributed + Redis Event MeshP95 SYNCHRONIZED
swarm_orchestrator.dispatch_dag(job_id='sw-901')

Neural Swarm & Edge Runtimev5.3-OPTIMIZED

Distributed task orchestration, sub-15ms INT8 vision inference, and zero-hallucination vector memory serving production workloads.

JITTER:
CONCURRENCY: 250 req/sZERO DRIFT
// 06. HOW I WORK · SYSTEMATIC RIGOR

ENGINEERING LIFECYCLE

From raw data to continuous production: the systematic discipline that separates toy prototypes from dependable, production-ready AI software.

01Problem Formulation & Baseline Establishment

RESEARCH SPECIFICATION

Understand the fundamental business constraint before writing model code. Review existing literature, inspect data quality distributions, and establish the simplest possible non-machine-learning baseline (heuristics or logistic regression).

REPRODUCIBLE ARTIFACTS GENERATED:
data_contract.yamlnull_distribution.parquetheuristic_baseline.py
RIGOR CHECKPOINTS100% COMPLIANT
  • Audit class balance and temporal distribution shifts
  • Define strict quantifiable North Star metrics (e.g. P95 latency vs. recall)
  • Establish unambiguous test/train isolation contracts
stage_01_stdout.log
LIVE TELEMETRY
$python -m src.audit.data_contracts --dataset production_v3
[INFO] Ingesting 14,200 sample tensors from isolated split...
[AUDIT] Class imbalance ratio: 1:18.4 (Defect vs Normal)
[AUDIT] Kolmogorov-Smirnov distribution drift test: p=0.48 (STABLE)
[BASELINE] Logistic baseline established: ROC-AUC = 0.642
[STATUS] North Star contract verified: Target P95 < 25ms, Recall > 82%
Process PID: 4892 · Exit Code: 0 (SUCCESS)Deterministic Execution

DECISION BOUNDARY REVEAL

Hover or drag across the boundary to reveal how mathematical cross-validation replaces speculative engineering.

Empirical Rigor // 93.4% Recall

Intuition & Heuristics // 64% Recall

// 07. EMPIRICAL VALIDATION & MISSION CONTROL

TECHNICAL METRICS

INTERACTIVE CLASSIFICATION THRESHOLD CALIBRATOR

Drag the operational threshold to observe the empirical trade-off between Precision, Recall, and production scrap costs.

TARGET: P95 < 25ms · RECALL > 80%
DECISION BOUNDARY THRESHOLD (τ):0.48
τ = 0.10 (High Sensitivity / Max Recall)OPTIMAL SWEETSPOT: τ = 0.45 – 0.52τ = 0.90 (Conservative / Max Precision)
PRECISION
87.4%
False alarm suppression rate
RECALL (DEFECT CAPTURE)
79.8%
Safety-critical defect identification
F1 HARMONIC SCORE
83.4%
Balanced mathematical equilibrium
OPERATIONAL RISK COST
$11,588
Simulated monthly defect loss
Probability calibration verified via Brier Score (0.089) and Platt Scaling.Empirical ROC-AUC: 91.4% (5-Fold Stratified)
STATUS: OPEN FOR AI/ML & DATA SCIENCE ROLES
TLS 1.3 SECURE
// 07. TRANSMISSION CONSOLE & DISPATCH

LET'S BUILD
SOMETHING USEFUL.

Whether you are architecting low-latency computer vision pipelines, multi-agent RAG systems, or seeking an AI/ML Engineer who ships validated mathematical code into production—I'd love to talk.

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Ask about Computer Vision pipeline latency...

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PRIMARY COMMS
anujmark.edwin.ame@gmail.com
Monitored daily with an average turnaround under 12 hours.
COMPOSE EMAIL
CODEBASE & REPOSITORIES37+ REPOS
github.com/anujmundu
Production PyTorch models, YOLOv5-CASP, LangGraph swarms, and FastAPI backends.
EXPLORE CODE REPOSITORIES
PROFESSIONAL NETWORKVERIFIED
linkedin.com/in/anujmundu
Full career trajectory, published architectures, and recommendations.
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