Available for opportunities
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I design and operate production systems end to end — from Django and FastAPI services to AWS infrastructure, CI/CD pipelines, and AI integrations. My work sits at the intersection of backend engineering and DevOps: shipping reliable APIs, automating deployments, and making complex platforms easier to run in production.
I'm a hands-on engineer who enjoys owning problems from the database layer through to deployment and monitoring. Whether it's tuning PostgreSQL queries, wiring up HaloPSA integrations, or standing up Terraform-managed AWS environments, I focus on systems that stay maintainable long after the first release.
I'm a Backend & DevOps Engineer with 3.5+ years of experience building cloud-native applications for MSP and enterprise environments. Most of my work involves turning operational pain points — slow reports, fragile deploys, scattered logs — into automated, observable platforms that teams can depend on daily.
On the application side, I build with Python (Django, FastAPI), design REST APIs, and work closely with PostgreSQL, Redis, RabbitMQ, and MongoDB. On the infrastructure side, I provision and manage AWS (EC2, ECS, RDS, S3, Lambda), containerize with Docker, orchestrate with Kubernetes, and automate delivery through Jenkins, GitHub Actions, and Terraform.
I've also spent significant time integrating PSA platforms like HaloPSA, workflow tools such as Rewst and n8n, and AI services including LLMs, RAG pipelines, and ElevenLabs — always with an eye toward practical production use, not demos that fall apart under real load.
I hold an AWS Solutions Architect Associate certification (SAA-C03) and care deeply about the operational side of engineering: structured logging, useful alerts, clear runbooks, and deployments that don't wake anyone up at 2 a.m.
I validate cloud architecture skills through industry certifications and keep them current as AWS services and best practices evolve. This certification reflects hands-on experience designing resilient, cost-aware systems on AWS — not just exam prep.
These are the tools I reach for most often in production. I'm comfortable going deep on a stack rather than skimming many — especially where backend services, cloud infrastructure, and automation overlap.
Designing and running workloads on AWS with infrastructure as code and container orchestration.
Building APIs, data pipelines, and integrations — including PSA platforms like HaloPSA.
Automating delivery, infrastructure changes, and MSP workflows with modern pipeline tooling.
LLM integrations, voice AI, and the monitoring stack needed to run systems reliably.
I've spent the last several years at IT By Design working across backend services, cloud infrastructure, and automation for MSP operations — often bridging gaps between product engineering and the teams running systems in production.
Full-stack ownership across Python backends, AWS infrastructure, PSA integrations, and internal automation — supporting live customer workloads in a fast-moving MSP environment.
Selected work that reflects how I approach engineering problems — reliable backends, thoughtful infrastructure, and automation that saves real time for the people operating the system. Each project below combines multiple layers of the stack, not just a single tool or framework.
A document Q&A system that lets users upload files and ask natural-language questions against their content. Built with FastAPI and LangChain, it chunks documents, generates embeddings, stores vectors for retrieval, and uses RAG to ground LLM responses in source material — reducing hallucinations and supporting multi-document sessions.
An internal knowledge platform that indexes documentation, wikis, and runbooks so teams can search conversationally instead of digging through folders. Django powers the API layer, PostgreSQL stores metadata and conversation history, and Redis handles caching — with source attribution on every answer so users can verify responses.
A deployment automation platform for shipping services to AWS with confidence. Jenkins orchestrates the pipeline, Docker packages applications, Terraform provisions infrastructure, and Kubernetes runs workloads in production. Supports blue-green releases, one-click rollbacks, and Slack notifications so the team always knows what shipped and when.
A real-time reporting system built around HaloPSA data for MSP operations. Django and Celery handle ingestion and scheduled jobs, PostgreSQL stores aggregated metrics, RabbitMQ queues heavy workloads, and AWS hosts the production environment. Teams get dashboards and scheduled email reports without waiting on manual exports.
A unified monitoring layer across Splunk, CloudWatch, Prometheus, and Grafana — giving one place to trace incidents from alert to root cause. Custom Python Lambdas enrich events, dashboards surface service health, and alert rules prioritize actionable signals over noise. Cut mean-time-to-detect production issues by 70%.
A modular Terraform framework for spinning up complete AWS environments — VPC networking, security groups, ECS clusters, RDS databases, and S3 buckets — from reusable modules with remote state. Designed so new environments stay consistent, auditable, and quick to provision without copy-pasting boilerplate.
A small arcade game built into this site — catch deploys and infrastructure components, avoid bugs and outages. It's a light break from reading, but also a nod to the kind of fast feedback loops I try to build into real pipelines: ship often, catch problems early, keep production healthy.
Pro tip: Release items are worth the most points.
I'm open to backend, DevOps, cloud engineering, and platform roles — especially where I can own services from code to production. If you're building something that needs reliable APIs, solid AWS infrastructure, or smarter automation around MSP and enterprise workflows, I'd like to hear from you. I typically respond within a day.