Andrés Conti
Backend Engineer · Applied AI · Data Engineering
I am Andrés Conti, a Computer Engineer and Backend Engineer. I help teams and businesses organize processes, connect tools, and build simple, maintainable solutions that are ready to use.
Services
The first version of a solution does not need to be huge. It needs to be well thought out, solve a concrete problem, and leave a foundation that can evolve.
Automation for repetitive tasks, document processing, information classification, reporting, and integrations between tools that currently require manual work.
Design and implementation of APIs, backend services, and system integrations with a focus on maintainability, technical clarity, and deliveries that can grow without becoming disorderly.
Data pipelines, exploratory analysis, dataset preparation, and first models to validate whether a problem can be solved with data or machine learning.
Starting point
A strong first step can be reviewing the process, idea, or pending integration and leaving with a clearer technical scope.
From there we can decide whether the right next move is an automation, a backend component, a data prototype, or simply an honest work plan that avoids overbuilding.
Start with a diagnosticBackground
My background combines backend software work, Computer Engineering training, and recent projects in data, machine learning, and applied AI. I especially enjoy turning complex problems into concrete, sustainable solutions.
Academic foundation
Training in software engineering, distributed systems, architecture, data, and machine learning. My thesis let me go deeper into big data pipelines and ML models applied to a real technical problem.
Professional experience
Experience developing and evolving backend services, APIs, and integrations in contexts where maintainability, reliability, and technical clarity matter. I am especially comfortable translating complex problems into implementable solutions.
Data and machine learning
Experience building data pipelines, ETL processes, exploratory analysis, and machine learning models on large-scale datasets with tools such as Spark, Python, and PyTorch.
Project in exploration
A personal project exploring bookings, automation, and operational experience for businesses that work with appointments. It is a space to validate product ideas, applied AI, and integrations with simple channels such as web or messaging.
Computer Engineering thesis
A project focused on building data pipelines and machine learning models to analyze transmission quality in optical networks over large-scale experimental datasets.
View repositoryProblem
The goal was to turn complex optical network data into usable information for deciding whether a connection could be established with sufficient quality.
Result
A reproducible pipeline, exploratory analysis, and model comparison across different representations of the network state.
How I work
01
I turn the problem into a concrete process: what happens today, what hurts, what repeats, and what outcome is expected.
02
I define what is worth solving first, what evidence we need, and what scope is reasonable for an initial delivery.
03
I work through clear deliveries: diagnostic, automation, integration, prototype, or functional backend component.
04
I document what matters, explain how to use the solution, and keep it maintainable or ready to evolve later.