AI Engineer — NLP & Intelligent Systems. I ship end-to-end intelligent systems — from LLM pipelines and retrieval stacks to production Laravel migrations — with a bias toward trustworthy, auditable design.
Infrastructure
Four AWS deployment strategies race head-to-head with real infrastructure — no mocks, no fakes. Click start and watch actual millisecond measurements stream in live.
AWS Lambda with forced cold start — no warm container reuse
AWS Lambda kept warm via EventBridge ping every 5 minutes
Fastify on bare EC2 (t3.micro) behind Nginx — port 3001
Containerized Fastify via docker-compose on EC2 — port 3002
Races all 4 strategies simultaneously — typically 1–3 seconds
AI Demo
A live AI agent powered by Claude Haiku. Pick a task, provide input, and watch it reason, select tools, and produce a structured report — all streamed in real time.
Select a task type, provide input, and watch the agent reason in real time.
Featured work
Autonomous 10-step AI migration pipeline for Laravel codebases.
CLI-driven migration system that ingests legacy Laravel code and runs analysis → planning → transformation → validation → self-healing loops. Processes 10k+ LOC per run at under $80 cost, with 85–90% automated issue resolution.
Four-study pipeline for automated Requirements Engineering — structured parsing, context-aware classification, and implicit keyword discovery.
End-to-end research system behind my MSc thesis at Ontario Tech University. The pipeline converts raw stakeholder prose into audit-grade knowledge artifacts across four studies: 1. **Studies 1 & 2 (CASCON 2025)** — Multi-model LLM parsing (GPT-4.1, Claude Opus 4, Meta-70B) into High-Level JSON artifacts with versioned tag governance (v0 → v2; v2 precision 0.95 / F1 0.85). 2. **Study 3 (RE'26 Main, under review)** — Sentence-level requirement classification with structured local-context features (+16 F1 at k=1; best F1 0.894 on 4K domain-aligned samples). 3. **Study 4 (RE'26 RE@Next!, in revision)** — 5-phase implicit keyword discovery over a 275,164-edge enriched co-occurrence graph, UMAP+HDBSCAN cluster-gap detection, and a bounded LLM-as-judge tiebreaker. 4. **Cross-cutting infrastructure** — 1,997 HLJ artifacts, 13,725-entry domain dictionary, FAISS index over 768-dim SBERT vectors, full per-decision audit logging, and drift monitoring across pipeline runs.
Open source
Experience
Ontario Tech University · Oshawa, ON
Palomino Systems · Remote
Mediabridge · Remote
Finserve Infotech · India
Research
A four-study pipeline that automates requirements engineering (RE) using NLP and AI — from raw stakeholder prose to implicit domain knowledge discovery. The work spans structured JSON parsing, transformer-based classification with local context, and multi-signal keyword discovery, grounded in an industrial FinTech and SaaS corpus.
Supervisor: Prof. Sanaa Alwidian
A modular multi-model LLM pipeline that converts raw stakeholder requirements into High-Level JSON (HLJ) artifacts with confidence-scored fields, paired with a versioned tag-governance system that catches prompt-leak exploitation, hallucinated tags, and low-agreement outputs before they reach downstream stages.
Explicit keyword extraction — even at audit-grade precision — hits a structural ceiling of roughly 1.5 canonical keywords per HLJ artifact, with Jaccard agreement below 0.11 across KeyBERT, RAKE, and YAKE. This paper presents a 5-phase implicit keyword discovery engine combining a 13,725-entry domain dictionary, a 275,164-edge enriched co-occurrence graph, UMAP+HDBSCAN clustering, and a bounded LLM-as-judge that tiebreaks only the borderline scoring band. Every implicit keyword traces back to its discovery signal(s) with full per-signal evidence.
An empirical study of sentence-level requirement classification on 110 real-world FinTech and SaaS documents (~5,700 candidate sentences, balanced to 15K mixed / 4K domain-only). We compare all-mpnet-base-v2 (110M) and DeBERTa-v3-base (184M) across frozen / LoRA / full fine-tuning and context window sizes k=0–3, and show that structured local-context features — not raw concatenation — are the critical signal.
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Capabilities
Contact
Happy to chat about NLP pipelines, LLM governance, autonomous migration systems, or graduate research collaboration. Email is the fastest way through.