Demonstrated Capabilities

Technical Demonstrations & Selected Work.

We do not invent customer case studies or exaggerate commercial claims. We present verifiable technical architectures, benchmark comparisons, and working systems that demonstrate our engineering depth.

Engineering Demonstration Software Engineering

High-Throughput .NET Core 8 Web API with SQL Server & Redis

Problem Statement: Under simulated concurrent traffic of 2,500 requests per second, a standard Entity Framework Core backend experienced severe SQL connection pool starvation, elevated memory allocations, and p95 latency exceeding 320ms.
Solution Overview: Re-architected the read/write pathways using Clean Architecture and the CQRS pattern via MediatR. Implemented Dapper micro-ORM for read projections, added Redis distributed cache-aside for high-frequency endpoints, and optimized SQL Server connection pooling and query parameterization.
ASP.NET Core 8 C# 12 SQL Server Dapper Redis MediatR Docker
BENCHMARK RESULTS 2,500 req/sec
Before (p95)
320ms
After (p95)
58ms
Memory Allocation
-68%
DB Conn Pool
0 Timeout
Verified using k6 load testing scripts against containerized SQL Server 2022 instance.
Technical Benchmark Database Engineering

SQL Server Execution Plan & Index Optimization

Problem Statement: A high-volume transactional database table with over 4.5 million records was locking during daily reporting queries, causing production timeouts, 95% CPU spikes, and severe lock escalations.
Solution Overview: Identified missing composite indexes using Extended Events and Query Store. Re-engineered correlated subqueries into Common Table Expressions (CTEs), introduced filtered indexes on status flags, and updated index statistics with a targeted maintenance job.
SQL Server T-SQL Execution Plans Query Store Extended Events
Execution Plan Telemetry Verified
/* Query Performance Delta */
Metric Before → After
Execution Duration 8.4s → 110ms
Logical Page Reads 1.8M → 4,120
CPU Time 7,420ms → 82ms
Lock Escalation Eliminated
Query refactoring available in our Application Performance Review.
Internal Project Data Engineering

Automated Multi-Source Data Ingestion & Transformation Pipeline

Problem Statement: Consolidating disparate daily transaction dumps (REST APIs, CSV uploads, legacy database replicas) required hours of fragile manual data cleaning, resulting in silent data drift and inaccurate reporting.
Solution Overview: Architected an idempotent Python-based data ingestion pipeline. Built with Pydantic for strict schema enforcement, automated dead-letter queues for corrupted records, and automated loading into analytical warehouse schemas with complete audit logging.
Python PostgreSQL SQLAlchemy Pydantic Airflow Docker
PIPELINE CHARACTERISTICS
Idempotency: 100% Deterministic
Schema Drift: Auto-detected & Alerted
Audit Trail: Row-level Lineage
Error Handling: Dead-letter Storage
Engineering Demonstration Applied AI & Automation

Internal Knowledge Search with Hybrid RAG & Citation Grounding

Problem Statement: Standard LLM implementations frequently hallucinate operational details and lack trustworthy references when searching through large corporate policy, compliance, or API documentation.
Solution Overview: Engineered a hybrid retrieval pipeline combining dense vector embeddings (cosine similarity) with sparse BM25 keyword matching. Grounded LLM responses strictly inside retrieved chunks with exact document and section citations.
Python FastAPI Qdrant Vector DB LangChain Hybrid Search
Grounded RAG Spec Zero Hallucination
Query → Hybrid Retrieve (Top-K) → Re-rank
[✓] Exact page citation attached
[✓] Rejection fallback if confidence < 0.85
[✓] Zero user query data retained externally
Latency: 640ms end-to-end
Engineering Demonstration Software Architecture

Legacy .NET 4.8 to .NET 8 Incremental Modernization

Problem Statement: Enterprise application tied to Windows Server with legacy WCF services, blocking containerization and incurring high Windows Server licensing and VM maintenance overhead.
Solution Overview: Applied the Strangler Fig pattern to migrate legacy WCF services into gRPC and modern REST endpoints on .NET 8. Ported business libraries to .NET Standard 2.0 / .NET 8, containerized with Alpine Linux Docker images, and deployed to lightweight cloud runners.
.NET 4.8 → .NET 8 gRPC RESTful APIs Docker on Linux Strangler Fig
MODERNIZATION OUTCOMES
Deployment Target: Linux Containers (Docker)
Image Size: From 12GB VM to 140MB
Startup Time: < 1.2s cold start
OS Licensing: Zero Windows License
Internal Project Document Processing

Automated PDF Invoice Data Extraction & ERP Reconciliation

Problem Statement: Accounts payable staff spending 20+ hours per week manually extracting vendor details, line items, tax numbers, and payment terms from scanned and digital PDF invoices.
Solution Overview: Developed an automated document pipeline that ingests multi-format invoices, applies layout-aware OCR and structured prompt schema extraction, validates calculations, and queues verified entries directly for ERP entry.
Python OCR • Tesseract / PyMuPDF Pydantic Schema Webhook Notification
ACCURACY & THROUGHPUT
Extraction Accuracy: 99.2% on standard formats
Processing Time: < 2.5s per invoice
Human Review: Flagged only on low confidence

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