Remote Senior ML Engineer for Customer Intelligence

Shiji-Us-Careers; Tijuana; Remote; MXN 900,000 - 1,300,000; Full time

Job summary

NTT DATA is seeking a Sr AI ML Engineer (Remote) to join the team in Guadalajara, MX. You will develop scalable ML models, collaborate with data scientists and engineers, and translate business needs into data-driven solutions. Strong Python/SQL, PySpark, and cloud ML tools are preferred.

The role emphasizes production-quality code, data pipelines with Spark or Airflow, and agile collaboration with product managers and stakeholders across the data science office.

Qualifications

• Bachelor’s degree in a quantitative field (Data Science, CS, Engineering, Math, Stats)
• 2–4 years of experience in machine learning or data engineering roles
• Proficiency in Python and SQL; PySpark experience is a plus
• Familiarity with TensorFlow or PyTorch; cloud ML tools desirable

Responsibilities

• Develop and implement ML models (e.g., recommender systems, segmentation, predictive analytics) to support business initiatives
• Collaborate with data scientists and engineers to translate requirements into solutions
• Write clean, production-grade Python/SQL code and build data pipelines with Spark and Airflow
• Support model validation, testing, and deployment processes
• Communicate findings clearly to technical and non-technical stakeholders
• Work in an Agile environment with product managers, data scientists, and engineers

Skills

Python

SQL

PySpark

TensorFlow

PyTorch

AWS Sagemaker

Databricks

Airflow

Education

Bachelor’s degree in Data Science, CS, Eng, Math, Stats

Tools

Spark

GitHub

Power BI

Tableau

Job description

NTT DATA is seeking a Sr AI ML Engineer (Remote) to join the team in Guadalajara, MX. You will develop scalable ML models, collaborate with data scientists and engineers, and translate business needs into data-driven solutions. Strong Python/SQL, PySpark, and cloud ML tools are preferred.

The role emphasizes production-quality code, data pipelines with Spark or Airflow, and agile collaboration with product managers and stakeholders across the data science office.