Mallikarjuna Mudiam – Blog Index

Explore key projects and engineering solutions that reflect my experience building scalable, cloud-native platforms across fintech, commerce, healthcare, civic services, and AI-powered systems.

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🔹 Commerce Personalization Platform (GCP)

A scalable e-commerce microservices platform with autoscaling APIs, Pub/Sub messaging, and CI/CD on GCP.

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🔹 Card Issuing Platform (Azure)

PCI-compliant card issuing and transaction APIs with service mesh, API Management, and CI/CD pipelines.

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🔹 Cold Chain Medical Logistics (AWS)

Healthcare-grade fulfillment and inventory tracking system built on Amazon EKS and Aurora with real-time alerts.

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🔹 Constituent & Voter Management (AWS)

80M+ voter engagement platform with XLS ingestion, PDF mailer generation, dashboards, and 200K volunteer support.

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🔹 Banking Platform – Salesforce + Mulesoft + AWS

Integrated customer, card, and loan servicing across CRM, mobile, and backend banking using APIs and EKS.

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🔹 AI Recommendation Engine (GCP)

Vertex AI-powered personalization system using Pub/Sub, Dataflow, Redis, and A/B testing to increase conversions.

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🔹 AI Chatbot + API Documentation (Issuing)

OpenAPI-integrated chatbot experience for reducing developer onboarding time and surfacing intelligent API help.

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🔹 Pipeline-Driven Agentic AI for Engineering

A vision for replacing prompt-based AI workflows with pipeline-driven, multi-agent engineering systems that enforce testing, validation, and token optimization.

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🔹 Blueprint: AI-Augmented Software Delivery That Actually Scales

A working blueprint for scaling software delivery with AI — redesigning workflows around three pillars: architecture, team topology, and process. Profitable AI means fewer steps, not faster ones.

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🔹 Blueprint: Why Most Agentic AI Projects Fail — And the Operating Model That Prevents It

A blueprint for deploying agentic AI in payment processor customer service without joining the majority of projects that get canceled. Map first. Systematize second. Automate third.

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🔹 Blueprint: Proving Healthcare AI Agents Before They Ship

A reference architecture for building and rigorously testing a portfolio of healthcare AI agents — care-gap, member outreach, HCC risk-adjustment coding, and trial matching. Rules-as-oracle, quality gates in CI, fault-injection meta-tests, SAST/DAST for agents, and calibrated LLM-as-judge evaluation. Prove it, don't hope.

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Want to explore how these patterns could apply to your platform? Reach out via LinkedIn or mmudiam@mmudiam.com.