MARIANGLEN LOUIS
IengineerAutonomousAIAgentsandMLinfrastructure,focusingoncreatingrobust,audit-readysystemsforthenextgenerationofintelligentautomation.
Who I Am
I build agentic AI systems and the ML infrastructure underneath them. The thread across my work is orchestration, fine-tuning, and refusing to ship a model I haven't measured. I care about systems that survive production, not ones that only work in a demo.
MS Data Science, University at Buffalo (Dec 2025). Open to ML, AI, and MLOps roles. Available to relocate anywhere in the US.
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. Validated with a second, cross-family Claude Sonnet 5 judge to rule out self-preference bias in the RAGAS scores.
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, cross-validated by an independent Claude Sonnet 5 judge within ~0.25 points on every dimension.”
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
VoltCast: 24-Hour US Electricity Demand Forecasting
End-to-end serverless MLOps system that forecasts 24-hour electricity demand for 4 US grid regions with a from-scratch PyTorch Transformer. Full production loop (ingest, validate, train, register, serve, drift-monitor, retrain) runs on scheduled GitHub Actions with a DagsHub MLflow registry, S3 storage, and a live Next.js dashboard.
Key Impact
“From-scratch PyTorch Transformer that beats a naive baseline by 32.8% (1,050 MW MAE) and serves as champion in all 4 regions, retrained automatically through a drift-gated, serverless GitHub Actions pipeline.”
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.”
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% → 58.5% accuracy and 6.46 → 1.71 perplexity drop while training only 0.67% of parameters. Published to HuggingFace Hub with automated metric injection, plus a K8s serving layer (vLLM + AWQ, HPA autoscaling, Grafana) load-tested at 326 req/s.
Key Impact
“0.3% → 58.5% 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, with a K8s vLLM+AWQ serving layer load-tested at 326 req/s.”
SQLora: Llama-3.2-3B Text-to-SQL Fine-Tune
QLoRA fine-tune of Llama-3.2-3B on 78K+ Text-to-SQL samples with end-to-end training, execution accuracy evaluation via Modal/SQLite, vLLM inference server, and automated HuggingFace Hub deployment pipeline.
Key Impact
“95.9% Execution Accuracy and 81.3% perplexity drop (25.84→4.82) on Text-to-SQL generation by fine-tuning only 0.67% of Llama-3.2-3B parameters via QLoRA.”
Professional Timeline
The Josh James Team, Keller Williams
AI Engineer
Part-timeBuffalo, NYAutomated real estate contract data extraction by building a Gemini multimodal OCR pipeline (FastAPI on Render) parsing property PDFs into Google Sheets, eliminating manual data entry for the KW transaction coordinator team.
University at Buffalo, Visual Computing Lab
Volunteer Research Assistant
Part-timeBuffalo, 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 the 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, flagging 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.
Nissha Medical Technologies
Data Scientist Intern (Capstone)
Part-timeBuffalo, 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.
Wipro Technologies
Lead Data Reliability Engineer
Full-timeBengaluru, IndiaEngineered Python automation suite replacing manual auditing with 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.
Open Source Contributions
Fix: sync invocation for async-only MCP tools in LangGraph
Identified a critical flaw in the existing fix for StructuredTool._run(): it rejected sync calls from inside a running event loop, the exact LangGraph case it was meant to solve. Proposed the correct two-path ThreadPoolExecutor pattern; author adopted it in a same-day force-push.
Diagnosed that PR #538's approach raised RuntimeError when called from inside a running event loop, failing silently in LangGraph's async context, the most common production use case.
Proposed two-path fix: `asyncio.run()` when no loop is running, ThreadPoolExecutor spawning a clean thread when one is. Pattern derived from production bridge code in Remedi.
Author adopted the approach in force-push commit `734e08f` the same day; fix enables LangGraph `ToolNode` to invoke async-only MCP tools synchronously without blocking or loop nesting.
Technical Arsenal
Skills & Stack
38 tools and frameworks used in production AI systems.
// 38 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
Looking to hire or have a project in mind? Send me a message.