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# AI Engineer Python, SQL & Data Pipelines

- **Company:** Acunor Infotech
- **Location:** Dallas, TX, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Data Validation, Information Engineering, Extract Transform Load (ETL), Data Mining, DevOps, Apache Hadoop, Hadoop Distributed File System, Apache Hive, Python (Programming Language), Machine Learning, Performance Tuning, Cloudera, Software Deployment, SQL Databases, Workflow Management Systems, Enterprise Data Management, Data Processing, Freeform SQL, Sql Optimization, Snowflake, Gitlab, Git, Data Pipelines, Control M
- **Published:** June 12, 2026
- **Apply:** https://www.dice.com/job-detail/6a3a20e0-3002-4243-8e91-c803145cc371

## About the Role

Must-Have Skills

 * Strong hands-on experience with Python for AI/ML development and deployment
 * Advanced SQL skills for data extraction, transformation, validation, and optimization
 * Experience building and maintaining ETL/ELT data pipelines
 * Experience with AI/ML model deployment and production support
 * Hands-on experience with Apache Airflow or similar orchestration tools
 * Experience with Snowflake
 * Strong knowledge of Cloudera, Hadoop, HDFS, and Hive
 * Experience with Git/GitLab and CI/CD pipelines
 * Understanding of data validation, monitoring, and workflow automation
 * Strong analytical, troubleshooting, and problem-solving skills

## Description

We are seeking a hands-on AI Engineer with strong expertise in Python, SQL, AI/ML model deployment, and enterprise data pipeline development. This role focuses on building and operationalizing AI solutions in production environments, including model scoring, ETL workflows, monitoring, scheduling, automation, and integration with enterprise data platforms., * Develop, deploy, and maintain AI/ML models using Python

 * Build scalable data pipelines for ETL, model scoring, monitoring, and automation
 * Write and optimize complex SQL queries for analytics and data processing
 * Integrate AI models and data workflows into enterprise platforms
 * Manage workflow orchestration using Airflow, Domino, or Control-M
 * Support production deployments, performance tuning, and monitoring
 * Collaborate with Data Science, Data Engineering, DevOps, and business teams


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