SR Machine Learning Engineer

Pacifica Continental; Ciudad de México; Presencial; MXN 600.000 - 900.000; Jornada completa

Descripción de la vacante

Pacifica Continental in Ciudad de México is hiring a SR Machine Learning Engineer to develop and maintain ML infrastructure for high transaction volumes. You will collaborate with global teams, mentor peers, and drive best practices to enhance product performance. Ideal candidates will have a Bachelor's degree in Computer Science or Engineering, 3+ years of experience, and proficiency in Python and related ML tools. This is an opportunity to impact users positively through technology in emerging markets.

Formación

• 3+ years of experience as a machine learning engineer or similar.
• Good verbal and written communication skills in English.
• High proficiency in Python with strong libraries understanding.
• Experience with cloud providers and data services.

Responsabilidades

• Collaborate with global teams to deliver data science products.
• Own the life cycle of ML infrastructure.
• Mentor other engineers on best practices.

Conocimientos

Machine Learning

Python

Data Engineering

Feature Engineering

Educación

Bachelor’s degree in Computer Science, Engineering, or related field

Herramientas

Scikit-Learn

Pandas

Flask

FastAPI

AWS

Chalk.ai

Tecton

Databricks

Feast

Descripción del empleo

SR Machine Learning Engineer

As a Machine Learning Engineer, you will play a critical role in developing and maintaining the core systems and infrastructure that power our data science applications. This position is platform/tooling focused.

You will work closely with other engineers, data scientists, risk/fraud analysts and product managers to build, maintain and improve the whole ML platform where our models and other DS products run. You will also develop tools that help our modeling team to create features, train, retrain, deploy, serve and monitor ML models.

In this role, you will own the process of creating and maintaining scalable tools and infrastructure that handle hundreds of millions of transactions per month, ensuring high performance and reliability with a focus on data as a principle. Your work will be instrumental to enhance the impact of the team as it will be a central point of serving both internal and external services.

You will be part of a data science team on a mission to improve access to credit and technology in emerging markets with the opportunity of creating a big and real positive impact to our millions of users across the countries we operate in.

Responsibilities

• Collaborate with global teams including Risk, Fraud, Engineering and Product to deliver world‑class data science products to international markets, including ML models, infrastructure and tools.
• Own the life cycle (design, development, deployment, delivery and monitoring) of the infrastructure that powers our ML models that serve 300 million transactions per month and ensure they have optimal performance.
• Drive the enablement of our modeling team by building new tools or adopting new technologies that will allow them to extract data, generate features and deploy/serve models with ease.
• Work with a data‑driven mindset and understand the critical importance of handling data properly and safely.
• Organize frameworks and develop processes in our codebase so that the easy and default coding style is cleanly structured.
• Mentor other engineers and data scientists about best practices in engineering.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field
• 3+ years of experience as a machine learning engineer, data engineer or a closely related position with a proven track record of writing production‑level code and developing and maintaining ML infrastructure.
• Good verbal and written communication skills in English
• Comprehensive knowledge of ML life cycle: from data extraction and feature engineering to model serving and monitoring for live and batch processing.
• High proficiency in Python and a strong understanding of its related libraries and frameworks (e.g., Scikit-Learn, Pandas, Flask, FastAPI, etc).
• Strong background in feature store implementation and usage (Chalk.ai, Tecton, Databricks, Feast, etc.), particularly focused on managing large volumes of data for online and offline processing.
• Demonstrated experience with cloud providers (AWS preferred) and related data services (e.g., databases, storage, serverless computing).
• Ability to work in a fast paced environment with constant requirement changes.