MARIANGLEN LOUIS
IengineerAutonomousAIAgentsandMLinfrastructure,focusingoncreatingrobust,audit-readysystemsforthenextgenerationofintelligentautomation.
Technical Work
A curated selection of my latest projects in AI Engineering, Data Science, and MLOps.
Agentic AI & RAG
REMEDI: Agentic AWS Security & Remediation Platform
A full-stack agentic security platform orchestrated by a 5-stage LangGraph pipeline with 8 parallel specialist sub-agents. Scans an AWS account across 8 services, auto-remediates vulnerabilities after human approval, and verifies fixes — backed by 21 MCP-compliant boto3 tools and a Next.js 15 dashboard.
Key Impact
"Audits 8 AWS services in parallel in under 5 minutes with zero unauthorized changes via a LangGraph human-in-the-loop safety gate and deterministic MCP tool dispatch."
AuditAI: Agentic RAG Compliance Engine
Architected an Agentic RAG system using LangGraph and CRAG to audit organizational policies against 4 major cybersecurity frameworks (NIST CSF 2.0, SP 800-53, ISO 27001, SOC 2). Optimized with parallelized retrieval, semantic routing, and real-time FastAPI SSE streaming.
Key Impact
"Audits policies against 4 frameworks (NIST CSF 2.0, SP 800-53, ISO 27001, SOC 2) simultaneously — achieving 96.7% RAGAS Faithfulness and 100% Context Recall via Corrective RAG with LLM-as-judge grading."
FinBuddy: AI-Powered Personal Finance Tracker
Developed a full-stack AI platform for personal finance tracking using GPT-4o Vision and OCR. Engineered a vector search architecture with pgvector and Supabase, and implemented an asynchronous insights engine for automated financial intelligence.
Key Impact
"Transformed raw financial visual data into semantically searchable assets with automated spending pattern analysis."
MLOps & Infrastructure
F1- Apex Guardian
Architected a cloud-native MLOps system for real-time F1 2026 telemetry monitoring using unsupervised Isolation Forest models for anomaly detection. Built a GitHub Actions-orchestrated 'Challenger' pipeline with automated KS-Test drift detection, AWS S3 feature storage, and a high-performance Streamlit dashboard.
Key Impact
"Optimized dashboard latency by 85% and enabled real-time anomaly detection for competitive racing telemetry."
CitiBike Demand Forecaster: Recursive ML & MLOps Pipeline
Built an end-to-end ML system for 24-hour City Bike demand forecasting with a recursive LightGBM engine and automated Champion/Challenger MLOps pipeline. Deployed a full-stack Next.js dashboard with geospatial visualization and AWS S3 data infrastructure.
Key Impact
"Engineered a high-precision recursive forecasting engine with automated drift detection and Champion/Challenger model promotion."
Computer Vision & Deep Learning
FinReason: Financial QA LLM (SFT + DPO)
Fine-tuned Qwen2.5-7B-Instruct on FinQA (SEC filings) via QLoRA SFT + DPO alignment. Achieved 0.3% → 56.5% accuracy and 6.46 → 1.71 perplexity drop while training only 0.67% of parameters. Published to HuggingFace Hub with automated metric injection and AWS SageMaker deployment scripts.
Key Impact
"0.3% → 56.5% accuracy and 6.46 → 1.71 perplexity drop on FinQA SEC earnings via QLoRA SFT + DPO, training only 0.67% of Qwen2.5-7B parameters."
llama-3.2-3b-sql-qlora
QLoRA fine-tune of Llama-3.2-3B on 19K+ SQL samples with end-to-end training, evaluation, vLLM inference server, and automated HuggingFace Hub deployment pipeline.
Key Impact
"94.6% perplexity drop (35.1 → 1.88) and ROUGE-L 0.909 → 0.986 on SQL generation by fine-tuning only 0.67% of parameters via QLoRA."
Professional Timeline
The Josh James Team — Keller Williams
AI Engineer
Buffalo, NYAutomated real estate contract data extraction — built Gemini multimodal OCR pipeline (FastAPI on Render) parsing property PDFs into Google Sheets, eliminating manual data entry for the KW transaction coordinator team.
Designed AI lead automation system connecting Brivity, Fello, and Mojo with AI-driven text follow-up — covering lead intake, re-engagement flows, and TCPA-compliant deduplication logic.
University at Buffalo — Visual Computing Lab
Volunteer Research Assistant
Buffalo, NYEngineered Pearson correlation pipeline across 2.1M DAM price rows and 26,304 weather records for NYPA's Virtual Power Plant research — identified transmission congestion as dominant LBMP driver (r=0.89–0.95 across all 5 nodes, both seasons), outweighing temperature by 3×; finding validated by project lead.
Implemented percentile-based outlier detection (p1/p99) across 32 Long Island transmission nodes — flagged 1,312 high-price hours averaging $292–$492/MWh (peak $1,323 at Huntington), attributing spikes to east-end transmission bottleneck and hour-of-day capacity constraints.
Built RTM resampling pipeline converting 2.19M 5-min interval records to hourly aggregates across 3 spot months — produced hour-of-day correlation analysis revealing morning ramp (hours 8–10) as peak weather-price coupling window for VPP dispatch targeting.
Nissha Medical Technologies
Data Scientist Intern (Capstone)
Buffalo, NYEngineered a real-time Computer Vision quality control system using YOLOv8 Nano and OpenCV to inspect 30M+ daily tickets, achieving 88.1% mAP and sub-100ms inference to eliminate high-speed production bottlenecks.
Developed a defect analysis pipeline evaluating pixel color intensity and bounding box dimensions, capturing 86.67% of critical micro-defects while maintaining 88.45% precision to ensure no good material was wasted.
Built a predictive maintenance module tracking dimensional drift and color deviation over time, establishing warning thresholds (Delta E > 15) to proactively alert operators before production failures occurred.
Wipro Technologies
Lead Data Reliability Engineer
Bengaluru, IndiaEngineered Python automation suite replacing manual auditing — programmatic validation of row counts, schema parity, and type consistency across SQL Server → Snowflake migrations feeding downstream ML pipelines.
Implemented source-to-target data integrity checks ensuring 100% structural accuracy across full pipeline; reconciled Power BI KPIs against Snowflake ground truth to validate ML-consumed aggregates.
Validated transformation outputs in SQL and Python against data modeler specifications, catching schema and logic deviations before corrupted records reached model training inputs.
Directed sub-team of 2 on SQL development and validation methodology; conducted technical reviews of test scenarios and automated validation scripts.
Technical Arsenal
Skills & Stack
37 tools and frameworks used in production AI systems.
// 37 skills · 5 categories
Education
Master of Science
University at Buffalo, SUNY
Data Science
Bachelor of Technology
Visvesvaraya Technological University
Electronics & Communication
Certifications
Introduction to Model Context Protocol
Anthropic
Claude 101
Anthropic
Claude Code 101
Anthropic
AI Engineering Core Track
Udemy
AI Engineer Agentic Track: The Complete Agent & MCP Course
Udemy
Machine Learning A-Z: Hands-On Python & R
Udemy
LET'S TALK
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