ML Platform Engineer: MLOps, Cloud & Production Systems

Amtex Enterprises Inc.; Northern (KY); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Amtex Enterprises Inc. is seeking a Machine Learning Platform Engineer to design, build, and support enterprise ML capabilities.

The role emphasizes MLOps, cloud infrastructure, automation, and production deployment of ML workloads across multiple locations, with a strong focus on security and reliability. The candidate will lead end-to-end ML development, design scalable architectures, and mentor engineers while collaborating with data science teams and stakeholders to achieve strategic

Qualifications

• Strong experience in ML development and lifecycle management.
• Hands-on MLOps and production ML systems.
• Expertise in Python and ML frameworks/libraries.
• Experience with Azure and/or AWS cloud platforms.
• Deep understanding of distributed systems and scalable architectures.
• Proficiency in Docker, Kubernetes, and Infrastructure as Code tools.
• Demonstrated technical leadership and mentoring experience.
• Strong written and verbal communication skills.

Responsibilities

• Lead end-to-end ML solution development including feature engineering, model training, and evaluation.
• Design and optimize ML models for performance and scalability.
• Work with Azure ML Studio or equivalent ML platforms.
• Ensure best practices in data preparation, experimentation, and reproducibility.
• Design production-grade ML pipelines with batch and real-time inference.
• Manage ML lifecycle: deployment, monitoring, versioning, governance, retraining.
• Establish CI/CD pipelines and robust ML workflows.
• Provide technical leadership and align ML initiatives with business goals.

Skills

Python

Machine Learning

MLOps

Cloud platforms

Docker

Kubernetes

CI/CD

Distributed systems

Technical leadership

Communication

Tools

Docker

Kubernetes

Terraform

ARM/Bicep

Job description

Amtex Enterprises Inc. is seeking a Machine Learning Platform Engineer to design, build, and support enterprise ML capabilities.

The role emphasizes MLOps, cloud infrastructure, automation, and production deployment of ML workloads across multiple locations, with a strong focus on security and reliability. The candidate will lead end-to-end ML development, design scalable architectures, and mentor engineers while collaborating with data science teams and stakeholders to achieve strategic