I’m Sam Abraham — a Senior Software Engineer, Cloud Architect, and AI/ML practitioner based in South Florida. I’ve spent the past two decades building software across industries that have very little in common with each other, and that breadth has been one of the most valuable parts of my career.

Having been fascinated with programming from an early age, I pursued a Bachelor’s in Computer Science in the Honors Program at Stony Brook University, graduating Summa Cum Laude. Fast forward twenty-plus years, and I’ve worked at companies ranging from large publicly traded enterprises to small, specialized product shops — and the mix has been a blessing.

Large companies taught me how to operate at scale: how systems behave under real load, how teams coordinate across functions, how architecture decisions ripple through an organization. But the smaller companies are where I learned to do everything. When you’re one of two or three engineers building a patented maritime safety system or shipping a customer service platform on a framework that’s still in beta, you don’t get to specialize. You own the architecture, the implementation, the deployment, and the consequences. That kind of exposure shaped how I think about systems more than any single role at a larger company could have. Earning my PMP and CSM gave me fluency in how projects are delivered and how agile teams operate — making me a stronger collaborator across disciplines.

One thing that stands out when I look back is how many different verticals I’ve worked across: financial services and retirement planning, mortgage and fintech, maritime safety, education technology, enterprise payroll and HR, authentic sport merchandise and warehouse fulfillment, and enterprise software R&D. Each industry came with its own constraints, compliance requirements, data sensitivities, and definitions of what “good enough” actually means — and moving between them forced me to stay adaptable rather than comfortable.

That range matters more now than it ever has. As AI tools become embedded in engineering workflows, domain knowledge is what separates useful output from plausible-sounding output. Knowing how a retirement plan administration system actually works, or what compliance means in fintech versus maritime versus education — that context is something no model brings on its own. Engineers who have operated across industries carry a library of patterns, constraints, and failure modes that makes them better collaborators with AI, not just better prompt writers.

I remain a hands-on software engineer, cloud engineer, and architect working with AWS and Azure — but now leaning heavily into AI. I help my team modernize through scalable, intelligent systems, with a particular focus on AI enablement, automation, and cloud-native architecture. I’m especially interested in the practical side of AI-assisted development — not the hype, but the real workflows and tradeoffs that engineering teams face as these tools become part of daily work.

If you want to connect, you can find me on LinkedIn.