Summary
Engineer with hands-on experience building backend systems, AI-powered document processing pipelines, and self-hosted LLM inference infrastructure. Currently pursuing a Master's in Computer Science (OMSCS) at Georgia Tech with graduate-level coursework in Artificial Intelligence and Machine Learning. Background spans industrial automation (PLC/SCADA) at SCIO Automation/CEC Controls and embedded systems and defense hardware testing at BAE Systems.
Education
Georgia Institute of Technology
Jan 2024 – May 2028
M.S. Computer Science
AI (CS6601), Knowledge-Based AI (CS7637), Deep Learning (CS8001/NVIDIA DLI), Computer Vision (CS6476), Machine Learning for Trading (CS7646, ongoing).
University of New Hampshire
May 2019
B.S. Electrical Engineering
University of Massachusetts Lowell
Post-graduate work in Radar Systems
Certifications
- NVIDIA Deep Learning Institute – Fundamentals of Deep Learning (2025)
- NVIDIA DLI – Applications of AI for Anomaly Detection (2025)
- NVIDIA DLI – Building AI-Based Cybersecurity Pipelines (2025)
- NVIDIA DLI – Generative AI with Diffusion Models (2025)
Experience
Controls Engineer · SCIO Automation / CEC Controls
08/2022 – Present
- Programs and maintains PLC systems using Allen-Bradley platforms (CompactLogix / ControlLogix) and SCADA systems including VTScada for real-time monitoring and control.
- Supports system commissioning, debugging, and field integration across multiple project sites; interprets electrical drawings and control schematics.
- Developed and deployed Watchtower, an internal vulnerability tracking system to monitor CISA advisories and firmware risks across customer panels and industrial control systems, supporting automated ingest, filtering, and long-term tracking of firmware/OS-level exposures.
- Built and integrated a self-hosted DeepSeek AI server on a high-performance AMD EPYC platform to support local document querying and LLM-powered engineering insights.
- Designed a multi-stage AI pipeline for interpreting industrial specification PDFs and addenda, generating structured engineering outputs (requirements lists, out-of-scope items, IO mappings, and page references).
- Implemented PDF parsing, prioritization logic, and structured JSON/Markdown outputs using Python, PyMuPDF, FastAPI, and PostgreSQL/SQLite.
- Integrated local LLM inference (Qwen/DeepSeek) and vector search (LocalRecall) to support RAG-based document querying and reasoning.
- Collaborated with senior engineers and IT staff to ensure alignment with compliance, uptime, and security expectations across multiple client sites.
Electrical Engineer 2 · BAE Systems
Jul 2021 – Aug 2022
- Supported design and analysis of electrical systems, reviewed schematics and hardware configurations for compliance with engineering standards.
- Participated in testing, validation, and troubleshooting of electronic systems; worked with cross-functional teams on system integration.
- Developed automation scripts in MATLAB and VBA to streamline engineering workflows and reduce manual data processing.
- Reverse-engineered and debugged legacy hardware modules, identifying root causes of system failures.
Electrical Engineer 1 · BAE Systems
Jun 2019 – Jul 2021
- Collected and analyzed test data to validate system performance under demanding operational conditions for advanced defense projects.
- Created Excel VBA macros to simplify data extraction from large engineering datasets, improving efficiency for cross-functional teams.
- Reviewed 100+ technical documents and schematics for compliance, submitting and supporting detailed engineering change requests (ECRs).
- Authored repeatable procedures for firmware loading and system sanitization, enabling consistent handoff to lab technicians.
Selected Projects
Personal RAG & LLM Knowledge Base
Personal · Python, FastAPI, SQLite, Ollama, Qwen3, DeBERTa, BM25, HyDE, PyMuPDF
Local-first RAG system with no cloud dependency. A DeBERTa-based 11-intent classifier routes each query to RAG, web search, conversation history, or direct LLM answer. Retrieval uses HyDE + BM25/RRF hybrid scoring with MMR diversity. Includes an Extraction Studio that digests large documents into structured markdown reports via a scan → rehydrate → synthesize pipeline. Web UI has SSE streaming, collection scoping, conversation history, and a document library browser. Achieves 90% accuracy on a 20-question golden eval.
Applied AI & ML — OMSCS Coursework
Academic · Python · CS6601, CS7637, CS6476, CS7646
Four completed graduate courses: implemented search algorithms (BFS/A*/minimax), probabilistic models (Bayesian nets, HMMs, GMMs), a full CV pipeline (HOG+SVM detector, Kalman/Particle tracking), and a quantitative trading system with Q-learning/Dyna RL agent.
Aura – Personal AI Assistant
Personal · FastAPI, SQLite/PostgreSQL, FCM, OpenAI APIs
Personalized assistant for task tracking, short/long-term goals, push notifications, and daily message generation. Modular task/goal schema with FCM push logic.
Technical Skills
Languages & Web
- Python, JavaScript, TypeScript
- Node.js, React, Next.js, HTML, CSS
- FastAPI, Flask, Uvicorn
- Basic: C, C++, C#, Java
AI / ML & LLM Systems
- Local LLM inference & self-hosted infra
- Embeddings & RAG pipelines
- Vector search, OpenAI API
- Prompt engineering, PyMuPDF
Data, Tools & Engineering
- SQL, PostgreSQL, SQLite
- MATLAB, VBA, Excel Macros
- Git, Linux, VS Code, PyCharm
- PLCs, HMIs, SCADA, Lab instrumentation