Machine Learning Engineer

Genius Business Solutions; Cleveland (OH); On-site; USD 140,000 - 220,000; Full time

Job summary

Genius Business Solutions Inc. (GBSI) seeks a Senior Machine Learning Engineer to design, build, and deploy enterprise-scale ML models and pipelines using Python, TensorFlow/PyTorch, and cloud platforms (AWS/Azure/GCP).

This role covers data prep, model training, optimization, and production deployment with MLOps practices. Key responsibilities include end-to-end ML system design, model evaluation, and collaboration with data engineering and DevOps teams to deliver scalable solutions.

Qualifications

• Strong Python expertise with NumPy, Pandas, Scikit-learn.

Responsibilities

• Design, build, deploy, and maintain ML models and end-to-end ML pipelines.
• Perform data preparation, feature engineering, model training, evaluation, and optimization.
• Deploy and monitor models in production, including model drift detection and retraining.
• Collaborate with data engineering, DevOps, and business teams to deliver scalable ML solutions.
• Architect enterprise-scale ML systems and drive MLOps, automation, governance, and reliability initiatives.
• Mentor engineers, provide technical leadership, and evaluate emerging AI/ML technologies.

Skills

Python

NumPy/Pandas

TensorFlow

PyTorch

ML/DL pipelines

Docker/Kubernetes

AWS/Azure/GCP

MLOps

Model deployment

CI/CD

Tools

MLflow

SageMaker

Azure ML

Docker

Kubernetes

Spark

Ray

Job description

About Genius Business Solutions Inc. (GBSI)

Featured in CNBC, Digital Journal, Fox News, and CIO Review, GBSI is a globally recognized IT services leader with 20+ years serving Fortune 500 organizations. Our teams deliver solutions across automotive, manufacturing, retail, and pharmaceutical industries. Headquartered in Moline, IL, GBSI supports clients and consultants throughout the US, Canada, Europe, and India.

Join our innovative team to lead enterprise-grade SAP solutions and drive digital transformation initiatives across global enterprises.

Job Summary

The Senior Machine Learning Engineer will design, build, and deploy enterprise-scale ML models and pipelines using Python, TensorFlow/PyTorch, and cloud platforms (AWS/Azure/GCP). This role involves end-to-end ML system development, including data preparation, model training, optimization, and production deployment with MLOps practices.

Primary Skills: Python, Machine Learning, TensorFlow, PyTorch, MLOps, AWS/Azure/GCP, Docker, Kubernetes

Required Skills

• Strong Python expertise with NumPy, Pandas, Scikit-learn.
• Hands-on experience with TensorFlow and/or PyTorch.
• Experience building and deploying ML solutions on AWS, Azure, or GCP.
• Knowledge of ML pipelines, model evaluation, CI/CD, version control, and MLOps practices.
• Strong understanding of ML system design, performance optimization, and monitoring.

Preferred Skills

• Experience with MLflow, SageMaker, Azure ML or similar MLOps platforms.
• Knowledge of Docker, Kubernetes, Spark, or Ray.
• Experience with LLMs, Deep Learning, Transformers, Vector Databases, and ML Governance.
• Prior experience leading ML projects or mentoring teams.

Key Responsibilities

• Design, build, deploy, and maintain ML models and end-to-end ML pipelines.
• Perform data preparation, feature engineering, model training, evaluation, and optimization.
• Deploy and monitor models in production, including model drift detection and retraining.
• Collaborate with data engineering, DevOps, and business teams to deliver scalable ML solutions.
• Architect enterprise-scale ML systems and drive MLOps, automation, governance, and reliability initiatives.
• Mentor engineers, provide technical leadership, and evaluate emerging AI/ML technologies.

Equal Employment Opportunity

GeniusBSI is an Equal Opportunity Employer. We believe that no one should be discriminated against because of their differences, such as age, disability, ethnicity, gender, gender identity and expression, religion, or sexual orientation.

All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status, or any other basis as protected by federal, state, or local law.