Junior AI/ML Engineer: Data & Cloud Platforms (Hybrid)

re-zoo-me; Dallas, Northern (TX, KY); Hybrid; USD 85,000 - 120,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Mod Op, LLC is seeking a Junior AI/ML Engineer (Data & Cloud Platforms) with 1–2 years of experience to help build scalable data systems and AI initiatives. You will work across data pipelines, cloud services, and ML workflows to deliver data-driven solutions.

This hybrid role requires in‑office presence in Dallas, TX two days per week, with the rest remote. You will collaborate with data engineers, analysts, and marketing teams to implement ETL/ELT pipelines, dashboards, and ML models using

Qualifications

• Hands-on experience with at least one cloud platform (GCP, AWS or Azure).
• Strong Python and data science libraries (Pandas/NumPy/Scikit-learn).
• Experience with SQL/NoSQL databases for data engineering tasks.
• Experience building dashboards with Looker or Tableau.
• Familiarity with ML workflows and cloud ML services.

Responsibilities

• Design, develop and maintain scalable ETL/ELT pipelines across cloud platforms (GCP, AWS, Azure).
• Integrate structured and unstructured data from CRM and marketing platforms for analytics.
• Develop and optimize queries for SQL/NoSQL databases.
• Support ML model development, training and deployment in cloud environments.
• Build dashboards to communicate insights to business and marketing teams.

Skills

Python

Pandas/NumPy/Scikit-learn

Data engineering

Cloud platforms (GCP/AWS/Azure)

SQL

Tools

Looker

Tableau

Dataflow

Airflow

BigQuery

Teradata

SageMaker

Vertex AI

Job description

Mod Op, LLC is seeking a Junior AI/ML Engineer (Data & Cloud Platforms) with 1–2 years of experience to help build scalable data systems and AI initiatives. You will work across data pipelines, cloud services, and ML workflows to deliver data-driven solutions.

This hybrid role requires in‑office presence in Dallas, TX two days per week, with the rest remote. You will collaborate with data engineers, analysts, and marketing teams to implement ETL/ELT pipelines, dashboards, and ML models using