Duque Ortega Mutis · Mexico City · open to junior ML / MLOps roles
Building ML systems
that survive production.
Three services deployed on GKE and EKS. Three production incidents measured, root-caused and fixed. One open-source template that packaged the lessons. Built on fourteen years of running operations before the first model.
Portfolio
one monorepo · three production services
Fourteen years of operations flow into the build: churn, financial NLP and demand forecasting — tested, deployed and operated on GKE and EKS.
The work
Projects, problems, solutions
Each service, the production problem it hit, and the fix — measured, root-caused and documented. Keep scrolling — the cases slide through.
BankChurn Predictor
81% of requests failing. The model was fine.
A load test exposed an 81% error rate on the BankChurn API. Root cause:
uvicorn --workers inside Kubernetes — multiple workers competing
for one shared CPU budget produce thrashing, not parallelism. Redesigned the
inference path with asyncio plus a ThreadPoolExecutor (GIL analysis documented),
errors dropped to zero and the CPU request was halved.
NLPInsight Analyzer
The heavier model we chose not to ship
Financial sentiment classification where the production question mattered more than the leaderboard: a transformer would score higher and cost more to operate, explain and debug. NLPInsight ships a lightweight, explainable path — and documents the rejected alternative as an engineering decision, not an omission.
ChicagoTaxi Pipeline
A score too good to be true — until it was
Demand forecasting over 6.3M Chicago taxi trips. The first model looked perfect because a feature was leaking the future into training. Removed the leak, rebuilt validation as strictly temporal, and the R² of 0.96 that survived honest re-evaluation is the one published.
Template
every lesson, packaged as defaults
The ML-MLOps Production Template turns the portfolio's production lessons into a reusable open-source system — the second project, and the strongest proof of product thinking.
- 32 anti-patternsD-01..D-32, each with its corrective action
- 28 ADRsdecisions with rejected alternatives
- SLSA L2 supply chainsigned images + SBOM attestation
- AUTO · CONSULT · STOPagent behavior protocol
- 6 env×cloud overlaysdev / staging / prod on GCP + AWS
- Governed AI-assisted devreviewable, bounded agentic coding
The toolbox
Capability map
ml engineering
- Python
- scikit-learn
- XGBoost / LightGBM
- SHAP explainability
- Pandera validation
mlops · serving
- FastAPI
- Docker · Kubernetes
- MLflow
- GitHub Actions CI/CD
- Prometheus · Grafana
cloud · infra
- GCP — GKE · Workload Identity
- AWS — EKS · IRSA
- Terraform
- Kustomize overlays
- Cosign · SBOM
data
- PySpark
- Pandas
- Temporal validation
- Drift — PSI · Evidently
- Leakage gates
governed ai-assisted development Claude Code · Cursor · Windsurf — behavior rules, skills, audit trail, eval gates. Engineered, not hidden.