Yash Shah · Chicago, IL
Distributed Systems · Backend & Platform Engineering · Cloud Architecture
.NET/C# · AWS · Kubernetes · Microservices · Event-Driven Systems
Building scalable systems, modernizing cloud platforms, and enabling engineering teams to deliver reliably.
Also exploring MCP and AI-assisted developer workflows where they improve engineering productivity.
Three areas where backend architecture, cloud delivery, and platform engineering come together.
Distributed APIs and partner workflows
Backend platforms connecting products, partners, and enterprise workflows — reliable APIs, authorization, event-driven systems, and maintainable cloud architecture.
AWS migration, infrastructure automation, reliability
Cloud-native modernization with Terraform, event-driven microservices, observability, and cost-aware platform decisions — including $1M+ annual infrastructure savings through AWS migration.
End-to-end fintech platform engineering
Live investor research platform — .NET backend, AWS cloud architecture, PostgreSQL, market-data pipelines, portfolio and research functionality, with AI-assisted research as one feature among many.
Five areas where architecture judgment, platform delivery, and technical leadership compound.
Event-driven architecture, messaging, high availability, and API design for systems that need to scale and stay reliable under load.
Microservices, REST APIs, and developer platforms on .NET — the backend layer product and partner teams build on.
AWS cloud-native delivery with Kubernetes, Terraform, containers, observability, and infrastructure automation — including $1M+ annual savings from platform modernization.
Architecture reviews, technical design, mentoring, cross-team delivery, and developer productivity initiatives.
MCP servers, AI-assisted development, and agentic workflow prototypes — practical tooling that improves how engineering teams build and operate systems.
Proof points — distributed backend systems, cloud modernization, fintech platform engineering, and developer productivity.
Distributed backend & platform
Daily Kubernetes batch job aggregating user data into Couchbase cohorts, surfaced via API for in-app CTA banners.
View on Projects →Cloud & platform engineering
$1M+ annual infrastructure savings through AWS modernization, event-driven microservices, and Terraform automation.
View on Projects →Backend & operational automation
Automated account onboarding workflows that eliminated ~25 human hours per day of manual operational effort.
View on Projects →End-to-end fintech platform engineering
.NET backend, AWS cloud architecture, PostgreSQL, market-data pipelines, and portfolio/research functionality — with AI-assisted research as one feature.
View on Projects →Developer tooling
MCP-based workflows, test scaffolding, triage support, and AI adoption patterns for engineering teams — with guardrails.
View on Projects →A compact view of where I've spent the last decade-plus. Full detail on the Experience page.
Chamberlain Group
Cloud platforms, enterprise integrations, partner APIs, and AI/DevEx workflows.
Morningstar
Financial technology platforms, AWS modernization, TAMP automation, and data workflows.
Kin + Carta
Cloud migration and domain-driven microservices for enterprise clients.
Blanket Finance
Live fintech research platform combining finance, data, and AI.
Published articles on innovation, agile delivery, and engineering architecture — plus drafts in progress.
How to move past "that's not how we do it here" — reinvent processes, share business problems openly, make failure acceptable, balance accountability with experimentation, and keep an open mind for ideas from anywhere.
Before you dial a customer care number, Google Phone already analyzed the business, estimated wait time, and surfaced busy-hour patterns — practical AI embedded in everyday workflows.
AI Engineering Series · Part 1 of 10
A practical guide for .NET engineers moving from chat prompts to RAG, MCP servers, agents, and agentic workflows — with security patterns, architecture diagrams, and platform mental models.
Reader favorites on Model Context Protocol and trading risk.
AI Engineering Series · Part 6 of 10
Understand MCP tools, resources, and prompts with practical examples for AI agents, enterprise workflows, and developer productivity.
AI Engineering Series · Part 9 of 10
A practical explanation of what MCP connectors expose to AI clients: tools, resources, prompts, and how to think about capability support.
Learn the 3-5-7 rule in trading as a simple risk management framework for position sizing, portfolio exposure, and avoiding concentrated losses.
Distributed systems, cloud-native platforms, and practical developer tooling — full breakdown on Stack.
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