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JannsenRamos/README.md

Albert Jannsen Ramos

AI/ML Engineer · LLM Systems & Applied ML

Building hybrid RAG pipelines, multi-agent systems, and explainable ML for high-stakes domains

Portfolio Email Location


🛠 Tech Stack & Ecosystem

Python SQL FastAPI Streamlit LangGraph FAISS LightGBM Docker AWS PostgreSQL


🔭 Highlights & Featured Projects

  • ArcSync — Architecture-Aware AI Specification Agent Full-stack multi-agent system generating a technical spec from any codebase in under 30s. 2nd of 300+ teams, IBM BOB Hackathon 2026

    • Architecture: 4-agent orchestration (IBM Watsonx.ai · Granite 3 8B) with a hybrid RAG pipeline — architectural file weighting, complexity scoring, domain synonym expansion
    • Key Features: FastAPI backend · 50MB ZIP ingestion · JSONL audit logging · 13+ languages supported
  • LARF — Legal Adaptive Routing Framework 5-stage LLM pipeline answering OFW legal questions across Philippine and Hong Kong law. Delivered to the Department of Migrant Workers under a formal MOA with Saint Louis University.

    • Architecture: Normalization → routing → hybrid retrieval (FAISS + BM25 + RRF) → generation → safety-auditing
    • Key Features: Coarse-to-strict reranking stage · low-confidence/unsafe-output flagging for human review
  • Incident Response Copilot — DevOps/SRE Multi-Agent RAG System 🚧 In Progress · Week 1 of 6

    • Architecture: Fine-tuned cross-encoder reranker (MLflow-tracked) over VOID postmortems + runbooks, orchestrated with LangGraph
    • Key Features: Triage → RCA → remediation → guardrail agents · Dockerized · GitHub Actions CI/CD · deploying to AWS
  • Credit Risk Modeling — Lending Decision Support System Calibrated LightGBM (Platt scaling) on 307k thin-file applicants, paired with a SHAP narrative engine explaining declines in plain English.

  • Telecom Churn Prediction — End-to-End ML Case Study LightGBM + CatBoost ensemble, 100k records, 140 features. Cost-sensitive threshold tuning moved recall from 65.7% → 91.5% — $5.18M net annual benefit.

  • Fraud Detection — Cost-Sensitive Transaction Modeling 🔄 Ongoing RandomForest on 6.3M imbalanced PaySim transactions, scored against a custom Net Error Revenue metric built on Philippine fintech cost assumptions.


GitHub & Activity Insights


Open to mid-level AI/ML engineering roles · Based in Baguio City, Philippines · Open to remote

Pinned Loading

  1. Caffe-in Caffe-in Public

    Semantic coffee discovery engine with portfolio-theory risk scoring — find your next cup by flavor language, not star ratings.

    Python

  2. ClearScore ClearScore Public

    A calibrated LightGBM credit scoring model with SHAP-based feature attribution and plain-English explanations, designed to meet EU AI Act high-risk AI requirements for transparency and the right to…

    Jupyter Notebook

  3. ArcSync ArcSync Public

    AI-powered feature spec generator that reads your actual codebase via IBM Bob before generating grounded technical specifications. Zero framework hallucinations.

    Python 1

  4. LARF-Legal-Adaptive-Routing-Framework LARF-Legal-Adaptive-Routing-Framework Public

    Forked from SLU-404FoundUs/LARF-Legal-Adaptive-Routing-Framework

    Python