Arthur Torres

AI Engineer

Production LLM agents & automation. Turning operational problems into shipped systems.

LLM agents in production at Banco Inter
1.4 FTE saved across 9 deliveries
37M telemetry records modeled

Belo Horizonte, Brazil

About

I build AI-powered systems and automations that solve real operational problems. Currently at Banco Inter's Global Operations, building LLM agents and Python automations that saved 1.4 FTE in 7 months. Previously at Akyou, where I built an end-to-end document intelligence pipeline using the OpenAI API.

Stack

PythonLLM AgentsFastAPIOpenAI APIGroqPyTorchDuckDBDockerKubernetes

Experience

Banco Inter

Data & Automation · Global Operations

Jan 2026 to Present

·Built and deployed LLM agents in production for contextual analysis supporting governance decisions
·Engineered Python automations orchestrating operational workflows, saving 1.4 FTE across 9 deliveries in 7 months
·Deployed automations as containerized microservices on the team's Docker, Kubernetes, and GitLab CI/CD platform

PythonFastAPILLM AgentsDockerKubernetesGitLab CI/CD

Akyou

Backend Engineer · AI & Accessibility

Apr 2025 to Dec 2025 · Remote

·Took a document intelligence product from scratch to a working prototype, owning it end to end
·Built the Python + OpenAI backend and LLM pipelines for unstructured PDF processing, extracting and restructuring complex documents into accessible, structured output
·Built the TypeScript frontend from the ground up and deployed the backend on AWS Elastic Beanstalk behind a load balancer

PythonTypeScriptOpenAI APILLM PipelinesAWS Elastic Beanstalk

Projects

Vale Desenvolver 2026: Mining Fleet Telemetry

Deep dive

End-to-end predictive pipeline to anticipate critical failures (Don't Go events) on 37M+ telemetry records from an iron ore mining fleet. Built for Vale's Programa Desenvolver 2026.

PythonPandasXGBoostLightGBMSHAPDuckDBscikit-learnpytest

GitHub Case study

Financial Intelligence Dashboard

End-to-end financial data pipeline with medallion architecture, LSTM trend classification and Text-to-SQL LLM interface.

PythonDuckDBPyTorchGroq APIStreamlit

GitHub Demo

Articles

May 3, 2026·8 min read
Designing Financial Features: The Math Behind RSI, MACD, and Rolling Statistics
A deep dive into the math behind common financial indicators: daily return, moving averages, RSI, MACD, and volatility, and how they transform raw market data into actionable signals.
Read

Skills

Languages

PythonSQLTypeScript

AI & LLM

LLM AgentsRAGOpenAI / Groq APIPyTorchLSTMXGBoost / LightGBMSHAP

Data

DuckDBPandasPlotlyStreamlit

Infrastructure

DockerKubernetesGitLab CI/CDAWS

Tools

FastAPIn8nGitGitHub

Practices

TDDSpec-Driven DevelopmentAgentic coding workflows

Certifications

Lean Six Sigma Yellow BeltEF SET C1 English (65/100)Claude Code 101

Contact

arthuroliveiratorres@gmail.com linkedin.com/in/torres-arthur github.com/tutorres