Software Engineer · Cloud Architect · AI Builder

I build reliable software
that scales quietly.

I help teams ship faster by migrating workloads to the cloud, automating the parts that should never be done by hand, and turning one-at-a-time processes into systems that run by the hundred.

See selected work Get in touch

github.com/mvmorten linkedin.com/in/mvmorten [email protected]

About

I'm a software engineer focused on the unglamorous parts of building software: making it run, keeping it running, and making sure the next person to touch it doesn't dread it.

I've worked across cloud migrations, infrastructure automation, and backend systems — including production work for a Fortune 500 company. I care about pragmatic engineering: solving the real problem, measuring the result, and removing toil rather than adding tooling.

I’m also a student of Don Norman’s The Design of Everyday Things. Human-centered design shapes how I approach APIs, internal tools, and anything someone else has to use — clarity, feedback, and getting out of the user’s way.

Currently open to staff/principal engineering roles, with a soft spot for AI.

$1 million+ Cumulative impact of a 2010 automation project at a Fortune 500 aerospace company — modeled at $100,000+/year × 15+ years, if still in service
100× Throughput increase by parallelizing a sequential workflow
100% IaC Migrated a fragile single-server app onto managed Azure, fully defined as infrastructure-as-code

Selected work

A few projects I keep coming back to in interviews — each one was a quiet, measurable win.

Case study 01 · Production LLM

Shipping GPT-4 into an internal CRM app

Problem

Users were spending real time reviewing and summarizing long conversation histories by hand. The friction held back engagement, and support was absorbing the slow turnaround.

Approach

Integrated Azure OpenAI / GPT-4 into the internal CRM to generate conversation summaries directly inside the user’s workflow — one of the earliest production LLM deployments I shipped. Worked through the practical constraints of putting an LLM in front of real users: data flow, evaluation, and safe rollout.

Impact

20% lift in user engagement and a 10% reduction in support response time — measurable proof that an LLM-assisted workflow could ship safely in production.

Azure OpenAI
GPT-4
Internal CRM
Production LLM

Case study 02 · Cloud migration

From a shoestring server to managed Azure

Problem

A revenue-generating web app was running on a single under-provisioned server with no redundancy. Outages were frequent and there was no path to scale.

Approach

Rearchitected the app for Microsoft Azure — managed app service, managed database, CDN in front of static assets, infrastructure defined as code so it could be rebuilt from scratch.

Impact

Cut response times, made the system reliably available, and made future changes safe to ship because nothing was hand-configured anymore.

Azure
App Service
Infrastructure as Code
CDN

Case study 03 · Scale

From one-at-a-time to hundreds in parallel

Problem

An automated process could only run a single job at a time. As demand grew, the queue grew with it, and the only knob anyone could turn was “wait longer.”

Approach

Restructured the workflow so each job was independent and idempotent, moved coordination off the host, and added concurrency controls so we could safely run hundreds of jobs simultaneously without stepping on shared resources.

Impact

Throughput went up roughly two orders of magnitude, backlog dropped to near zero, and the team got back the engineering hours they were spending babysitting the queue.