ML Engineer: Build & Deploy End-to-End AI Pipelines

TechDigital Group; Cincinnati (OH, AZ); On-site; USD 120,000 - 160,000; Full time

Job summary

TechDigital Group seeks an experienced ML engineer to design, build, deploy, and maintain end-to-end ML solutions in production. You will collaborate with data engineering, DevOps, and product teams to scale ML systems, optimize performance, implement MLOps governance, automate CI/CD, and ensure robust monitoring across cloud platforms.

Based in Arizona, this role offers leadership opportunities mentoring engineers and guiding architecture for enterprise-scale pipelines, governance, and

Qualifications

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

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

Machine Learning

TensorFlow

PyTorch

MLOps

AWS/Azure/GCP

Docker

Kubernetes

Tools

MLflow

SageMaker

Azure ML

Job description

JD:
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.