What are the responsibilities and job description for the Machine Learning Engineer position at CornerStone Technology Talent Services?
ML Data Engineer (Contract-to-Hire)
Location: Onsite 4 out of 5 days per week – Must be local or willing to relocate
Citizenship Requirement: US Citizen or Green Card holder only
Employment Type: Contract-to-Hire
CornerStone Technology Talent Services (TTS) is actively partnering with a nationwide industry leader on a high-impact contract-to-hire opportunity for a Machine Learning Data Engineer This role sits at the intersection of large-scale data engineering and applied AI, contributing directly to mission-critical initiatives that drive operational efficiency, predictive insight, and sustainable infrastructure solutions. If you’re passionate about transforming data into measurable impact and want to work in a role where innovation meets infrastructure—this is your seat at the table.
What You’ll Tackle:
- Architect and scale robust data pipelines to support ML model development, operational analytics, and real-time decision engines
- Lead the deployment and optimization of machine learning models across production systems to enhance performance and reliability
- Collaborate cross-functionally with analytics, infrastructure, and operations teams to ensure smooth integration of AI/ML solutions
- Continuously evaluate and enhance systems for reliability, scalability, and automation in a data-intensive environment
Your Tech Toolbox:
- Fluency in Python and SQL for model and pipeline development
- Hands-on experience with ML Ops tools such as PySpark, Databricks, TensorFlow, or PyTorch
- Strong working knowledge of data visualization tools like Tableau or Power BI
- Solid understanding of CI/CD pipelines, model lifecycle management, and version control tools (e.g., GitLab CI, Jenkins)
- Familiarity with cloud ecosystems (AWS, Azure, GCP)
Preferred Background:
- 3 years in ML engineering, optimization, or data pipeline architecture
- Bachelor’s or Master’s degree in Analytics, Computer Science, or related technical discipline
- Prior experience deploying ML models in large, dynamic enterprise environments
- Exposure to deep learning, infrastructure metrics, or operational reporting dashboards is a plus
Corp-to-Corp (C2C) candidates will not be considered
