Why This Role Stands Out
This ML Engineer role at Prudent Technologies and Consulting offers a fantastic opportunity to leverage your Python and SQL expertise in developing and deploying production-grade machine learning pipelines. You'll thrive here if you have a solid grasp of ML concepts and experience with enterprise data science platforms, contributing to innovative projects in a reputable company. Apply now to grow your career in a dynamic technology environment.
Quick Overview
Seniority
Mid Senior
Work mode
On Site
Location
San Antonio, TX, United States
Posted
21 hours ago
DockerSQLMachine LearningScrumSnowflakeAgileAirflowApacheHadoopHiveKanbanPython
Job Description
Job Title - ML Engineer
Location - San Antonia, TX. - Onsite
Duration: 6+ Months
USC-EAD /TN only
Job Summary
- Strong hands-on programming experience in Python, SQL for data processing, model implementation, automation, and production-grade pipeline development.
- Solid understanding of machine learning concepts, model lifecycle, model artifacts, scoring logic, feature engineering, and model validation.
- Hands-on experience with OpenShift and Docker or similar enterprise data science platforms.
- Experience building and managing workflow orchestration using Apache Airflow, including DAG creation, scheduling, dependency management, monitoring, and troubleshooting.
- Strong SQL skills and experience working with enterprise data platforms such as Snowflake, Hadoop, Hive, or relational databases.
- Experience with GitLab, GitHub, or similar version control and CI/CD tools.
- Ability to understand model development code and convert it into scalable, maintainable, and production-ready implementation pipelines.
- Experience with batch model scoring, data extraction, feature generation, post-processing, and output delivery processes. Knowledge of testing practices including unit testing, integration testing, regression testing, and production validation.
- Strong documentation skills with the ability to create implementation guides, deployment notes, runbooks, and technical specifications.
- Good understanding of Agile delivery practices and ability to work within Scrum or Kanban execution models. Strong problem-solving, debugging, communication, and stakeholder collaboration skills.
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