I'm an applied ai engineer with 3+ years in software engineering and a production-AI focus since 2024. I work across the system: data pipelines, APIs, retrieval, model integration, and the interface people use.
At Sevina Technologies I build healthcare automation, including clinical-compliance and reimbursement workflows. I describe that work at the system level here to protect confidential information. My independent projects make the architecture, implementation, and evaluations available to explore.
The work I enjoy most is connecting a useful model to reliable software: defining what it can do, handling failures, and measuring whether the complete system solves the problem.
How I think
How I approach the work
Evidence over demos
Publish the method, sample size, and limitations alongside the result. Where details are confidential, describe the engineering without disclosing the data.
Bound the model's authority
Validate generated output before execution. Use permissions, explicit rules, and refusal paths to limit what the model can do.
Design for the operating constraints
Free-tier hosting shaped the projects: scoped retrieval, bounded agent loops, and lean containers, with cold starts and quotas treated as part of the design.
Timeline
Growth over titles.
What I built, learned, got wrong, and changed — each phase includes the mistake, because that's where the learning is.
Independent AI products
2025 – present
Built
Four live AI products on a $0 stack — a NL→SQL agent (DBWhisper), an honest backtester (TradePulse), a +EV sports-ML system (CrownWager), and a multi-user AI chat platform (LLM Studio).
Learned
Safety-first LLM systems: deterministic gates around model output, retrieval over context-stuffing, and evaluation before pixels.
Mistake
Early on I trusted model output where a deterministic gate was needed — an LLM writing SQL against a live database with only a prompt between it and a mistake.
Changed
Fail-closed validators and 'look-ahead-free by construction' became defaults, not afterthoughts — the safety is now a property of the architecture.
AI/ML Engineer
Sevina Technologies
2024 – present
Built
Production healthcare-AI automation — clinical-compliance and reimbursement pipelines (MDS/PDPM), LLM document analysis with multi-provider routing and RAG, and eligibility-verification services, Dockerized and CI'd.
Learned
Building AI under real regulatory constraints (HIPAA): synthetic data, aggregate metrics, auditability, and knowing when the boring deterministic approach is the correct one.
Mistake
I leaned on an LLM for correctness that belonged in deterministic code — enumerable rules an auditor needs to reproduce.
Changed
Rules and validation moved into code the model can't override; the LLM assists, it doesn't adjudicate.
Junior Python Developer
Linescripts Software
2022 – 2024
Built
End-to-end web applications and RESTful services — a Hospital Management System, e-commerce platforms, and third-party API integrations, across Python, Django/DRF, and a JavaScript frontend.
Learned
How to ship maintainable software end to end, and that the interesting part is usually everything around the feature — the tests, the errors, the edges.
Mistake
I optimized for shipping features over tests, and paid for it later in regressions I could have caught.
Changed
CI and tests became non-negotiable before a feature is 'done' — the definition of done now includes proof it works.
