Architecture Library & Case Studies
Systems Built to Work in Production.
Deep dives into design constraints, architecture decisions, and measurable outcomes across stateful multi-agent AI, regulated clinical data, and high-throughput marketplace infrastructure.
AGENTIC AI & STATEFUL MULTI-AGENTSep 2024 — Present
Eva Tek · Enterprise Sovereign Agent Engine
Stateful multi-agent runtime on GCP with LangGraph, Redis checkpointing, MCP tool interfaces, and local SLM fallbacks.
-40% Latency & Token Overhead
Reduced inference latency and token consumption by over 40% via dynamic prompt compression and step-level caching.
Maintained 99%+ system uptime through high-availability Kubernetes runtime with automated fallbacks.
LangGraphModel Context Protocol (MCP)Redis CheckpointingFastAPI
Full BreakdownHEALTHCARE AI & DISTRIBUTED COMPUTEAug 2023 — Aug 2024
Johnson & Johnson · Cronus ML & Clinical Sensor Pipelines
Enterprise distributed ML experimentation framework built on PyTorch, Ray Tune, and GPU clusters, cutting multi-node experiment cycles by 60%.
60% Faster Experiment Cycles
Slashed multi-node training and hyperparameter search cycles by 60% across cross-functional research teams.
Reduced false-negative detection rates by 30% in production-validation environments for clinical actigraphy pipelines.
PyTorchRay TuneGraph Neural Networks (GNN)MLflow
Full BreakdownDISTRIBUTED BACKEND & EVENT-DRIVEN QUEUESSep 2022 — Jan 2023
Dubizzle Dubai · Real-Time Marketplace Event Streaming
Asynchronous microservices architecture and real-time event streaming across UAE's largest marketplace using Kafka, Celery, and Redis.
90% Reduction in Lead Latency
Cut enterprise client onboarding latency by 90%, significantly accelerating commercial partner velocity.
Handled high-throughput real-time lead streaming with zero data loss across millions of marketplace events.
PythonDjangoApache KafkaCelery
Full BreakdownFINANCIAL AI & RISK COMPLIANCEMar 2021 — Jun 2022
Unit21 & RMI Insights · Financial Risk & 3-Statement Automation
AI risk and compliance engines with Databricks and Snowflake, plus automated 3-statement financial modeling for fintechs.
80% Faster Financial Reconciliation
Automated 3-statement financial reconciliation for high-growth fintechs, reducing forecasting cycle times by 80%.
Scaled Databricks/Snowflake ETL pipelines to process millions of transactions daily with verifiable data consistency.
DatabricksSnowflakePyTorchGCP
Full Breakdown