End-to-End Data & ML Engineer (LATAM) — AI-Driven Pipelines

Medium; Chihuahua; Presencial; MXN 1.222.707 - 1.572.052; Jornada completa

Descripción de la vacante

Medium is looking for a skilled Data/ML Engineer in Mexico. This full-time role involves designing, building, and maintaining data pipelines and AI-driven applications within a collaborative team environment.

The ideal candidate will have extensive experience in data engineering and MLOps, strong communication skills, and proficiency in Python. Joining our BI team means playing an essential role in driving our data strategy, aimed at enhancing business decision-making and profitability.

Formación

• 8+ years of experience as a Data Engineer, with 2+ years in MLOps.
• Excellent English communication skills.
• Expertise in Python for data engineering tasks.
• Deep knowledge of vector databases and RAG architectures.
• Experience with cloud platforms like AWS or Azure Machine Learning.
• Hands-on experience with big data technologies including Apache Spark and Hadoop.

Responsabilidades

• Design and maintain scalable data pipelines for various workflows.
• Build workflows for extracting and retrieving unstructured data.
• Implement analytics and dashboarding solutions for insights.
• Manage prompt engineering and orchestration for conversational AI.
• Oversee vector data stores and develop indexing methodologies.

Conocimientos

Data Engineering

MLOps

Python

Vector Databases

SQL

Agile Methodologies

Apache Spark

Hadoop

Kafka

AWS

Azure Machine Learning

Descripción del empleo

Medium is looking for a skilled Data/ML Engineer in Mexico. This full-time role involves designing, building, and maintaining data pipelines and AI-driven applications within a collaborative team environment.

The ideal candidate will have extensive experience in data engineering and MLOps, strong communication skills, and proficiency in Python. Joining our BI team means playing an essential role in driving our data strategy, aimed at enhancing business decision-making and profitability.