About Me¶
Candidate profile
From operations to production ML¶
I'm Duque Ortega Mutis, I am based in Mexico City, and I am making my first formal career move into ML/MLOps.
My previous career was not technical by title, but it was technical in practice: I spent 14 years coordinating people, budgets, vendors, customer pressure, deadlines and process failures. That work taught me to value systems that are clear, measurable and usable by the next person responsible for them.
That is why this portfolio is built around production habits rather than only model scores. I am early-career in formal ML employment, but I am not new to ownership, trade-offs, documentation or operating under pressure.
How to read my seniority
Entry-level / junior in formal ML/MLOps employment. Experienced in ownership, pressure, cost-awareness and making systems easier for other people to operate.
What I Am Looking For¶
Target level
Entry-level / junior¶
I am looking for teams where I can contribute early, learn quickly and grow toward production ML work under experienced technical guidance.
Best-fit roles
MLOps, ML engineering and applied AI¶
Entry-level / junior MLOps or Production ML, Machine Learning Engineer, AI Engineer I, ML Platform or Data Engineering roles with ML workflows.
Operating lens
Systems people can trust¶
I care about testing, deployment, monitoring, cost, documentation and the failure modes that appear after a model leaves the notebook.
Career Timeline¶
2010-2024
Business operations¶
Teams, budgets, vendors, customer pressure and process ownership across multiple ventures.
2024-2026
Pivot to Data Science and MLOps¶
Applied ML projects, TripleTen training and a production-minded portfolio built around services rather than notebooks.
2026
Reusable MLOps template¶
Portfolio lessons packaged into a starter system with serving, CI/CD, deployment and operating guardrails.
Now
First formal ML/MLOps role¶
Looking for an entry-level / junior role where operational maturity can support real engineering growth.
Operations Experience In Numbers¶
Education And Certifications¶
Formal ML training
Data Science Professional Program — TripleTen¶
Completed in 2026. This is the formal training layer behind the portfolio work: applied machine learning, data workflows, evaluation and project delivery.
Cloud / platform evidence
Hands-on AWS practitioner¶
EKS, ECR, IRSA and Terraform exercised across the portfolio. Multi-cloud parity is documented in ADR-012 and ADR-013; infrastructure code lives in infra/terraform/aws/.
What I Can Contribute Early¶
ML APIs
Serving contracts¶
Build and test FastAPI inference services with clear request/response contracts.
Model workflows
Experiment and model packaging¶
Track experiments, package models and support model versioning with MLflow.
Deployment support
Runtime artifacts¶
Work with Docker, Kubernetes manifests, CI/CD and cloud deployment patterns.
Data workflows
Validation and feature work¶
Support feature engineering, validation, PySpark jobs and leakage checks.
Documentation
Readable operating knowledge¶
Write decision records, runbooks and project summaries that help teams review and maintain systems.
Reliability mindset
Measure before guessing¶
Look for failure modes, monitoring gaps and cost trade-offs before they become production problems.
How I Work¶
I like practical systems, clear ownership and honest measurement. If a model metric looks too good, I want to check for leakage. If an API fails under load, I want to measure before guessing. If a cloud setup is too expensive for the value it provides, I want to document the trade-off.
I am not presenting this portfolio as years of corporate ML experience. I am presenting it as evidence of learning velocity, engineering discipline and a strong fit for teams that need an entry-level / junior teammate who already thinks about reliability and business impact.
Simple working principle
Build the smallest system that proves the operating idea, then make the evidence clear enough that another engineer can review it.
What I Am Building Next¶
Live operations
More traffic-backed evidence¶
Run short, budget-controlled live windows to capture fresh metrics, monitoring screenshots and failure-response notes.
Team signal
More collaboration artifacts¶
Add public feedback, PR review examples and external review notes so the portfolio shows how I communicate in engineering conversations.
Technical depth
One focused infrastructure deep dive¶
Go deeper on one operational component, such as monitoring, deployment strategy or model-serving reliability, with trade-offs and examples.
Domain leverage
A project closer to real operations¶
Build a future ML project around staffing, inventory, cost anomalies or operations forecasting, where my background is directly relevant.
Current Boundaries¶
I try to be precise about what this portfolio proves. It shows controlled production-oriented evidence: live deployment windows, load tests, CI/CD, Kubernetes artifacts, screenshots, ADRs and incident-style debugging. It does not claim years of corporate ML platform ownership or continuous production traffic from real users.
That honesty matters to me because I want the first interview to start from the right place: formal entry-level / junior scope, strong operating maturity, and a clear path to grow inside a real team.
Best reading
Early-career in ML/MLOps employment; mature in ownership, documentation, cost-awareness and operational reasoning.
Links¶
GitHub profile¶
Source code, portfolio repositories and ongoing project work.
Portfolio source code¶
The repository behind this GitHub Pages site.
Video demo¶
A guided walkthrough of the portfolio and its operating evidence.