Senior AI/ML Engineer - Production-Ready Models (Remote)

Shure Latinoamérica; Victoria de Durango; A distancia; MXN 420.000 - 780.000; Jornada completa

Descripción de la vacante

NTT DATA seeks a Sr AI ML Engineer (Remote) to join our team in Guadalajara, Jalisco, Mexico. You will develop scalable ML models to boost customer intelligence and support business decisions, collaborating with data scientists and engineers across agile teams.

The role emphasizes production-quality Python, PySpark, and SQL, plus building data pipelines with Spark or Airflow, and model validation and deployment. Strong communication with stakeholders is essential.

Formación

• Bachelor's degree in a quantitative field.
• 2-4 years in ML or data engineering roles.
• Proficiency in Python and SQL; PySpark a plus.
• Experience with ML frameworks such as TensorFlow or PyTorch.
• Cloud experience with AWS Sagemaker or Databricks preferred.

Responsabilidades

• Develop and implement ML models such as recommendation engines, customer segmentation, and predictive analytics.
• Collaborate with data scientists and engineers to translate business requirements into technical solutions.
• Write clean, production-quality code in Python, PySpark, and SQL.
• Assist in building data pipelines using Spark, Airflow, or similar tools.
• Participate in model validation, testing, and deployment processes.
• Communicate findings to technical and non-technical stakeholders.
• Work in an agile environment with cross-functional teams.

Conocimientos

Collaborative teamwork

Communication

Problem solving

Educación

Bachelor's degree

Herramientas

Python

SQL

PySpark

TensorFlow

PyTorch

AWS Sagemaker

Databricks

Airflow

Descripción del empleo

NTT DATA seeks a Sr AI ML Engineer (Remote) to join our team in Guadalajara, Jalisco, Mexico. You will develop scalable ML models to boost customer intelligence and support business decisions, collaborating with data scientists and engineers across agile teams.

The role emphasizes production-quality Python, PySpark, and SQL, plus building data pipelines with Spark or Airflow, and model validation and deployment. Strong communication with stakeholders is essential.