Senior Machine Learning Engineer

TAG - The Aspen Group•Chicago, IL

1d•$154,000 - $181,000•Onsite

About The Position

The Aspen Group (TAG) is one of the largest and most trusted retail healthcare business support organizations in the U.S., supporting over 23,000 healthcare professionals and team members at more than 1,150 locations across 48 states. Our five supported healthcare practices operate under the brands Aspen Dental, ClearChoice, WellNow, Chapter Aesthetic Studio, and Lovet. We’re committed to enabling healthcare professionals to focus on patient care while we handle the business operations that support them. As part of our continued investment in data-driven innovation, we are looking for a Senior Machine Learning Engineer to join our growing team. This is a hands-on role focused on delivering innovative predictive solutions for the healthcare enterprise. The ideal candidate will bridge the gap between theoretical data science and production-grade software engineering, utilizing Google Cloud Platform (GCP) and Vertex AI to solve complex business challenges.

Requirements

5+ years of experience in machine learning engineering, data science, or related technical role.
Strong proficiency with Python and AI/ML frameworks (LLMs, TensorFlow, PyTorch, Scikit-learn, etc.).
Strong hands-on expertise with Google Cloud Platform (GCP), training, pipelines, deployment, BigQuery, and Airflow.
Solid understanding of software engineering principles, and API development.
Proven track record of building and deploying ML models into production systems at scale.

Nice To Haves

Bachelor’s degree in Computer Science, Data Science, Engineering, or related technical field.
Experience with scheduling, optimization algorithms, or decision-support systems.
Familiarity with MLOps tooling (e.g., MLflow, Airflow, Kubeflow).
Experience with SQL and data warehousing for feature extraction.
Familiarity with responsible AI practices and governance.

Responsibilities

Machine Learning Development & Modeling Design, develop, and implement predictive models, machine learning algorithms, and decision engines to address complex business challenges (e.g., scheduling optimization, clinical decision support).
Conduct experiments, tune hyperparameters, and apply advanced modeling techniques to improve the accuracy, scalability, and efficiency of new and existing models.
Partner with cross-functional stakeholders to translate business requirements into technical specifications for ML solutions.
MLOps & Production Engineering Execute full-stack engineering tasks including data preprocessing, feature engineering, model deployment, API development, and integration with enterprise systems.
Engineer scalable, secure, compliant, and production-grade ML pipelines with modern cloud-native technologies.
Design and implement distributed training workflows, online inference systems, and low-latency serving architectures.
Propose new platform solutions and enhancements that improve capabilities and performance of existing scheduling systems and decision engines.
System Architecture & Observability Partner with system architects to ensure adequate hardware, compute resources, and platform configurations (Kubernetes, Cloud Run) to support machine learning applications.
Define key metrics, monitoring frameworks, and automated alerts to continuously evaluate the performance and drift of deployed models.
Analyze system behaviors to identify improvement opportunities and proactively recommend enhancements to the decision engines.
Collaboration & Mentorship Create clear, comprehensive documentation and support guides for newly implemented tools.
Provide technical guidance and mentorship to junior engineers and data scientists.
Stay current with advances in machine learning, data engineering, and software development, implementing industry best practices for reliability and maintainability.