Job Details

Location:

Pittsburgh, PA, 15222 / Cleveland, OH, 44136

Salary Range:

Competitive and commensurate with experience

Introduction

Join our innovative team where you will have the opportunity to work on cutting-edge machine learning projects. We are seeking a skilled professional who is passionate about optimizing and maintaining large-scale feature engineering jobs, and who thrives in a collaborative, cross-functional environment.

Required Skills & Qualifications

Must-have qualifications that candidates must meet to be considered:

• Applicants must be able to work directly for Artech on W2
• Expert-level proficiency in Python, with strong experience in Pandas, PySpark, and PyArrow, 6+ years of experience required.
• Expert-level proficiency in Hadoop ecosystem, distributed computing, and performance tuning, 6+ years of experience required.
• 5+ years of experience in software engineering, data engineering, or MLOps roles, 6+ years of experience required.
• Experience with CI/CD tools and best practices in ML environments, 6+ years of experience required.
• Experience with monitoring tools and techniques for ML pipeline health and performance, 6+ years of experience required.
• Strong collaboration skills, especially in cross-functional environments involving platform and data science teams, 6+ years of experience required.

Preferred Skills & Qualifications

Nice-to-have skills but are not required:

• Experience contributing to internal MLOps frameworks or platforms.
• Familiarity with SLURM clusters or other distributed job schedulers.
• Exposure to Kafka, Spark Streaming, or other real-time data processing tools.
• Knowledge of model lifecycle management, including versioning, deployment, and drift detection.

Day-to-Day Responsibilities

Key tasks and expectations for the role:

• Optimize and maintain large-scale feature engineering jobs using PySpark, Pandas, and PyArrow on Hadoop-based infrastructure.
• Refactor and modularize ML codebases to improve reusability, maintainability, and performance.
• Collaborate with platform teams to manage compute capacity, resource allocation, and system updates.
• Integrate with existing Model Serving Framework to support testing, deployment, and rollback of ML workflows.
• Monitor and troubleshoot production ML pipelines, ensuring high reliability, low latency, and cost efficiency.
• Contribute to internal Model Serving Framework by sharing insights, proposing and implementing improvements, and documenting best practices.

Company Benefits & Culture

• Inclusive and diverse work environment that fosters innovation and collaboration.
• Opportunities for professional growth and development.
• Supportive team culture with a focus on work-life balance.

For immediate consideration please click APPLY to begin the screening process with Alex.