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# Junior AI/ML Engineer (Data & Cloud Platforms)

- **Company:** Mod Op
- **Location:** Dallas, TX, United States
- **Experience:** Starter
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, BigQuery, Business Systems, Cloud Computing, Cloud Database, Computer Programming, Databases, Data Cleansing, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Systems, Data Visualization, Data Warehousing, Data Flow Control, Data Intelligence, Python (Programming Language), Machine Learning, NoSQL, NumPy, Systems Development Life Cycle, Standard Sql, Azure Machine Learning, Software Engineering, SQL Databases, Tableau (Software), Teradata SQL, Data Processing, Google Cloud, Snowflake, Apache Spark, Google Vertex AI, Pandas, AI Platforms, Scikit Learn, Cassandra, Data Analytics, AWS Data Analytics, Machine Learning Operations, Virtual Agents, Azure Synapse Analytics, Looker Analytics, Data Pipelines, Software Library, Alteryx
- **Published:** November 4, 2026
- **Apply:** https://www.careerbuilder.com/job-details/junior-ai-ml-engineer-data-cloud-platforms-dallas-tx--6eaac6ef-6a3f-42ed-964a-ea115b58e5d5

## About the Role

Artificial Intelligence (AI), Software Engineering, Cloud Computing, Machine Learning, Artificial Intelligence (AI), Software Engineering, Cloud Computing, Scalable System Development, Automation, Customer Relationship Management (CRM), Cloud Platforms:
Hands-on experience with GCP, AWS, or Azure, particularly in data engineering or machine learning environments.

Programming Skills:
Strong proficiency in Python with experience using data processing and machine learning libraries such as Pandas, NumPy, and Scikit-learn.

Database Experience:
Experience working with SQL and NoSQL databases, including platforms such as BigQuery, Teradata, Cassandra, or similar technologies.

Data Visualization:
Experience building dashboards and reports using Google Looker or Tableau.

Machine Learning Knowledge:
Basic understanding of machine learning workflows, model training, evaluation, and deployment in cloud environments.

Data Automation & Transformation:
Experience with Alteryx or similar tools for workflow automation and data preparation.

Preferred Qualifications

Experience with data warehousing platforms such as Snowflake or Redshift.
Exposure to Apache Spark, Airflow, or other orchestration tools.
Familiarity with MLOps practices and cloud-based ML platforms.
Understanding of data governance, security, and compliance practices.
Relevant certifications such as Google Cloud Professional Data Engineer or Machine Learning certifications.

## Description

As a Junior AI/ML Engineer (Data & Cloud Platforms) with 1-2 years of experience, you will support the development of scalable data systems and contribute to AI and machine learning initiatives. This role combines data engineering, cloud technologies, and machine learning implementation to help build intelligent data-driven solutions.

You will work with AWS, GCP, and Azure cloud services, integrate with CRM and marketing platforms, and support analytics and AI solutions through tools such as Google Looker and Tableau. The role will involve working with both data pipelines and machine learning workflows to enable advanced analytics and automation.

The position operates under a hybrid work model, requiring in-office presence at the Dallas, TX location two days per week, with the remaining days worked remotely.

What you'll do

Data Pipeline Development:
Assist in designing, developing, and maintaining scalable ETL/ELT pipelines across cloud platforms such as GCP, AWS, and Azure, using services like Dataflow, Cloud Composer (Airflow), Azure Synapse, and AWS Data Pipelines.

Data Integration:
Work with structured and unstructured data sources, including CRM systems, marketing platforms, APIs, and internal business systems, to support analytics and AI-driven applications.

Database Management:
Develop and optimize queries for SQL and NoSQL databases such as Teradata, BigQuery, Cassandra, and cloud data warehouses.

Machine Learning Implementation:
Support the development and deployment of machine learning models using Python and data science libraries (Pandas, NumPy, Scikit-learn) and cloud AI services such as GCP Vertex AI, AWS SageMaker, or Azure ML.

AI-Enabled Data Workflows:
Assist in building data pipelines that support predictive analytics, automation, and AI-driven insights.

Data Visualization:
Build and maintain dashboards using Google Looker and Tableau to help business and marketing teams understand and act on data insights.

Collaboration:
Work closely with data engineers, analysts, data scientists, and marketing teams to understand business requirements and support the development of data and AI solutions.