Hi! I'm Gabrielly Gomes (call me Kimi 🧩) — ML Engineer focused on MLOps and AI Infrastructure with 2 years of experience building production AI systems that scale and cost less.
I'm currently building Billink AI — a SaaS platform that automates cloud and AI infrastructure cost auditing using multi-agent LLM pipelines.
- 💼 ML Engineer / MLOps Engineer at Cortex: Architected an Agentic AI system processing 80K+ stores across 700+ malls on Kubernetes, enabling a $2M revenue product line. Implemented monitoring, alerting, and batch inference pipelines.
- 🤖 AI Researcher at UFPI: Building hybrid BiLSTM-Transformer models for hate speech detection with feedback loop for retraining. Fine-Tune Baseline | BiLSTM Baseline
- 🏆 Transpetro/Petrobras Hackathon (7th place): Physics-Informed ML for industrial prediction — identified $1.89M in fuel savings and 1,198 tons CO₂ avoided. Paper | Dashboard | Model | REST API
- 🇧🇷 I Speak Kanoê: Built the first dataset for Kanoê indigenous language preservation. Documentation | Dataset
- 📊 Social Impact Research: Created Water Inequality Dataset of NE Brazil and published peer-reviewed research. Paper | GitHub
- 📊 Cloud Infrastructure Analyst (Freelance): Audited infrastructure handling 3.26B requests with 99.92% availability during Black Friday, analyzing EKS cluster efficiency and Spot Instance resilience.
- MLOps & Infrastructure: Kubernetes (EKS), Karpenter, Spot Instances, Docker, MLflow, CI/CD for ML, Model Monitoring, Drift Detection
- Cloud: AWS (S3, Lambda, EC2, EKS, DynamoDB, Cost Explorer), Terraform, FinOps
- Machine Learning: LLMs, Agentic AI, RAG, NLP, TensorFlow, PyTorch, Physics-Informed ML
- Data Engineering: PostgreSQL, ClickHouse, Supabase, ETL/Data Pipelines, Analytics Engineering
- Observability: Grafana, CloudWatch, HyperDX (P95 Latency, Error Budgeting)
- Programming: Python, SQL, PySpark, C++
- Stanford & DeepLearning.ai — Machine Learning Specialization. ML Project
- Harvard Aspire Institute — AI-Integrated Leadership Program (AILP) Alumna
- DataCamp — AI Engineer Associate & Data Engineer Associate
In parallel to my engineering work, I follow a self-directed curriculum mirroring Stanford's CS undergraduate program — driven by curiosity for theoretical depth that complements production experience. Tracking progress publicly keeps me honest.
Click to see full curriculum & progress
Focus: Mathematical rigor and algorithmic thinking.
Semester 1 — Nov/25 → May/26
- ✅ CS106A — Programming Methodology (Code In Place)
- ✅ CS106B — Programming Abstractions Real-Time systems - currently developing
- ✅ Introduction to Mathematical Thinking
- ✅ Introduction to Logic
- 🚧 CS103 — Mathematical Foundations of Computing
Semester 2 — Jun/26 → Dec/26
- ⏳ MATH19 — Calculus I
- ⏳ PHYSICS61 — Mechanics
- ⏳ CS107 — Computer Organization & Systems
- ⏳ CS109 — Probability for Computer Scientists
- ⏳ CS125 — Data & Society
- 🎯 Capstone Project 1
Semester 3 — Jan/27 → Jun/27
- ⏳ MATH20 — Calculus II
- ⏳ MATH51 — Linear Algebra & Multivariable Calculus
- ⏳ CS111 — Operating Systems Principles
- ⏳ CS221 — Artificial Intelligence: Principles and Techniques
Semester 4 — Jul/27 → Dec/27
- ⏳ CS229 — Machine Learning
- ⏳ CS161 — Design & Analysis of Algorithms
- ⏳ CS205L — Continuous Mathematical Methods for ML
- 🎯 Capstone Project 2
Semester 5 — Jan/28 → Jun/28
- ⏳ CS22N — Natural Language Processing with Deep Learning
- ⏳ CS259 — Algorithm Fairness
- 🎯 Capstone Project 3
Legend: ✅ done · 🚧 in progress · ⏳ planned · 🎯 capstone
- 🧑🏫 Teaching Fellow — Data and AI for Social Analysis course at UFPI
- 🧑🏫 AI Instructor — Teaching AI to women entering tech (NGO)
- 🇺🇳 United Nations MGCY — Major Group for Children and Youth volunteer
- 💼 LinkedIn: Gabrielly Gomes
- 📧 Email: gabrielly.gomes@billinkai.com