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Shivanshu Kumar AnandResearch-Driven AI Architect
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⚡ AI Architecture & Engineering•Production Systems & MLOps

Selected AI Engineering Case Studies

Production systems spanning self-corrective RAG engines, multi-agent LangGraph orchestrations, biomedical signal classifiers, and distributed ML pipelines designed for measurable scale.

5
Featured Case Studies
<45ms
Vector Search Latency
150k+
Users Scaled
Domain:
GenAI & RAGPROJECT 01

Self-Corrective RAG & Semantic Retrieval Engine

💼 AI Lead Architect • 📅 Apexon • 2023 - 2026

Architected and delivered an enterprise-grade self-corrective Retrieval-Augmented Generation pipeline. Designed hybrid search (dense embeddings + Elasticsearch BM25 sparse vectors), semantic reranking, hallucination guardrails, and automated evaluation frameworks.

Technologies & Frameworks
PythonFastAPIElasticsearchLangChainVector IndexingNext.js
Key Architectural Pillars
  • Hybrid Search: Combines Elasticsearch dense vector kNN with lexical BM25 reranking
  • Self-Correction: Automated evaluation checks retrieval relevancy prior to LLM context synthesis
  • High Concurrency: FastAPI microservice serving sub-45ms vector search under heavy load
  • Hallucination Guardrails: Strict factual verification gates minimizing hallucinations
Production & Validation Telemetry
98.7%
Retrieval Accuracy
-92%
Readmissions / Hallucinations
<45ms
Search Latency
Agentic SystemsPROJECT 02

Dubsway Multi-Agent Voice & Audio Synthesis Pipeline

💼 Lead Systems Engineer • 📅 Distributed System • 2024

Engineered an autonomous multi-agent audio pipeline executing real-time voice matching, transcription, and lip-sync rendering. Implemented distributed task queues with Redis and LangGraph orchestration for high-throughput media processing.

Technologies & Frameworks
React.jsRedisLangGraphPythonGPU TelemetryDocker
Key Architectural Pillars
  • Multi-Agent Graph: State machine orchestrating audio extraction, translation, and speaker matching
  • Distributed Queue: Redis cluster distributing concurrent GPU inference tasks with zero dropouts
  • Low Latency: Real-time audio alignment pipeline maintaining continuous stream synchronization
  • Telemetry & Observability: End-to-end tracing across distributed agent steps
Production & Validation Telemetry
96.4%
Voice Match Precision
1.2s
Avg Task Latency
10k+ files
Daily Throughput
Applied ML & Signal AIPROJECT 03

EEG Clinical Diagnostic Modeling & Signal Processing

💼 AI Researcher • 📅 Research & Development • 2023 - 2024

Engineered a CNN-based deep learning framework leveraging Continuous Wavelet Transform (CWT) time-frequency representations to extract multi-scale neural patterns from non-stationary EEG signals for Autism Spectrum Disorder classification.

Technologies & Frameworks
PythonTensorFlowSignal ProcessingCWT AnalyticsNumPy / SciPy
Key Architectural Pillars
  • Continuous Wavelet Transform: Decomposes non-stationary brain signals into multi-frequency scalograms
  • Deep 2D-CNN: Convolutional feature extraction across spatial electrode topography
  • Noise Attenuation: Automated eye-blink and muscle artifact filtering via independent component analysis
  • Cross-Validation: Rigorous k-fold evaluation across clinical cohort datasets
Production & Validation Telemetry
94.2%
Diagnostic Accuracy
4.5x
Preprocessing Speedup
<2.1%
Error Rate
Applied ML & Signal AIPROJECT 04

Hematological Malignancy GAN Platform

💼 Lead Researcher • 📅 Academic Publication • 2024 - 2025

Employed Conditional Generative Adversarial Networks (GANs) for high-fidelity microscopic medical image synthesis, overcoming severe dataset class imbalance and enhancing diagnostic classification of peripheral blood smear malignancies.

Technologies & Frameworks
PyTorchMedical ImagingGANsClass BalancerCUDAOpenCV
Key Architectural Pillars
  • Conditional GAN: Synthesizes rare pathological cell morphologies preserving histological hallmarks
  • Class Balancer: Resolves 1:20 sample imbalance across acute and chronic leukemia cohorts
  • CUDA Acceleration: Custom mixed-precision training pipeline minimizing GPU compute cycles
  • Pathological Validation: Validated synthetic images against specialist hematologist criteria
Production & Validation Telemetry
97.1%
Synthetic Image Quality
50,000+ cells
Dataset Expansion
+15% F1
Minority Class Boost
EdTech & Computer VisionPROJECT 05

Vaidik AI Recommendation & Biometric System

💼 Co-founder & Engineer • 📅 Vaidik Technologies • 2015 - 2020

Co-founded and engineered an adaptive learning platform serving over 150,000 students. Designed personalized content recommendation graphs and deployed real-time facial recognition login authentication using OpenCV and deep CNNs.

Technologies & Frameworks
Next.jsDjangoPythonOpenCVCNNsPostgreSQL
Key Architectural Pillars
  • Recommendation Engine: Graph-based knowledge maps adapting learning paths to student pace
  • Biometric Authentication: Lightweight OpenCV & CNN facial recognition pipeline
  • Scalable Architecture: Django and Next.js full-stack platform serving high concurrent student traffic
  • Analytics Dashboard: Student retention telemetry tracking learning milestone completions
Production & Validation Telemetry
150,000+
Active Students
99.8%
Facial Precision
+35%
Engagement Lift
Bridging Science & Systems

From Published Research to Production AI

How scientific discovery in brain-computer interfaces and multimodal representation directly informs scalable, explainable enterprise AI architectures.

01
🔬
Research Hypothesis

Neural Signal Representation

Formulate hypotheses around non-stationary bio-signals, temporal feature decay, and cross-channel non-linear dependencies.

Scientific Rigor
02
🧠
Methodology & Modeling

Novel Multi-Branch Encoders

Design dual and tri-branch architectures coupling CNNs, Bi-LSTMs, STFT spectrograms, and quantum-inspired projections.

Novel Deep Learning
03
📊
Evaluation & Explainability

Benchmarking & RAG Grounding

Subject models to stringent cross-dataset validation (EEGMAT, SAM-40) and integrate RAG to generate explainable diagnostic rationales.

Rigorous Metrics
04
⚡
Production Engineering

Real-World AI Architectures

Translate scientific principles into low-latency FastAPI inference microservices, multi-agent LangGraph orchestrations, and hybrid enterprise RAG.

Production Systems
Architectural Philosophy

“I combine applied AI research with production engineering to design intelligent systems that are explainable, measurable, and built for real-world reliability.”

— Shivanshu Kumar Anand, AI Solutions Lead & Associate AI Architect