Applied Machine Learning Engineer - ML & AI Systems

TAG - The Aspen Group•Chicago, IL

3d•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 and AI-driven innovation, we are expanding our machine learning capabilities across predictive analytics, optimization, and intelligent decision systems — while gradually incorporating modern Generative AI technologies where they create measurable business value. We are seeking an Applied Machine Learning Engineer to design, build, and deploy production-grade machine learning systems across the healthcare enterprise. This is a hands-on engineering role focused primarily on predictive modeling, optimization systems, decision engines, and scalable ML infrastructure. The ideal candidate combines strong modeling expertise with production engineering and MLOps experience. In addition to traditional ML, this role will have opportunities to explore and implement Generative AI and LLM-powered capabilities as part of TAG’s evolving AI roadmap. This role works in close partnership with Enterprise IT and Platform Engineering to ensure production-grade reliability and scalability.

Requirements

Bachelor’s degree in computer science, data science, engineering, or related technical field with 3-5 years of experience in machine learning engineering, data science, or related technical role.
Strong proficiency in Python and ML frameworks (Scikit-learn, TensorFlow, PyTorch, etc.).
Experience building and deploying production-grade ML systems at scale.
Strong hands-on experience with: Google Cloud Platform (GCP), Vertex AI (training, pipelines, deployment, monitoring), BigQuery and data warehousing
Experience with distributed systems, model deployment, and API development.
Solid understanding of software engineering principles and system design.

Nice To Haves

Experience with optimization algorithms or decision-support systems.
Familiarity with MLOps tooling (Airflow, MLflow, Kubeflow, etc.).
Exposure to Generative AI, LLM-based systems, or RAG architecture.
Familiarity with Kubernetes, Cloud Run, Dataflow, Pub/Sub.
Experience in healthcare or regulated environments.
Familiarity with responsible AI and governance best practices.

Responsibilities

Design, develop, and deploy predictive models and ML algorithms to address business challenges such as: Schedule Optimization, Propensity Segmentation, Demand Forecasting, and Pricing
Conduct experimentation, feature engineering, and hyperparameter tuning to improve model performance in collaboration with data scientists
Implement advanced modeling techniques including tree-based methods, deep learning, and optimization algorithms.
Translate business requirements into scalable ML solutions in partnership with cross-functional stakeholders.
Take ownership of model performance from experimentation through production monitoring.
Build scalable, secure, production-grade ML pipelines using modern cloud-native technologies.
Contribute to implementing distributed training workflows, batch and real-time inference systems, and low latency serving architectures
Deploy models via APIs and integrate with enterprise applications.
Leverage Google Cloud Platform (GCP), including Vertex AI, BigQuery, Kubernetes, Cloud Run, Dataflow, and Pub/Sub.
Implement CI/CD workflows for ML lifecycle management within defined architecture
Ensure reproducibility, versioning, and governance of models and features.
Define and track key performance metrics for deployed models.
Implement monitoring frameworks (e.g., Vertex AI Model Monitoring, logging, drift detection).
Analyze model behavior in production and proactively improve reliability and performance.
Collaborate with platform and infrastructure teams to ensure models meet scalability and compliance requirements.
Contribute to the development of LLM-powered solutions where appropriate (e.g., knowledge retrieval, decision-support copilots).
Support implementation of Retrieval-Augmented Generation (RAG) pipelines using BigQuery and Vertex AI.
Assist in experimentation with agent-based workflows and modern orchestration frameworks as part of TAG’s evolving AI initiatives.
Stay current with advancements in Generative AI and evaluate their practical application to enterprise healthcare use cases.
Work cross-functionally with Product, Platform, Data and Software Engineering teams
Create clear documentation for models, pipelines, and systems.
Promote engineering best practices across data science and analytics teams.
Contribute to continuous improvement of TAG’s ML platform capabilities.