AI/ML Architect

Description

We are looking for a Data/Information Architect to join a team in Madison, Wisconsin on a contract-to-permanent basis. This position focuses on building and advancing machine learning operations capabilities, with an emphasis on scalable data pipelines, model deployment, and practical forecasting solutions. The ideal candidate brings strong hands-on experience with Python, PySpark, and API-driven ML services, along with the judgment to select the right algorithms for different business problems.

Responsibilities:
Architect and enhance machine learning workflows that support reliable model development, testing, deployment, and monitoring.
Create scalable data pipelines using Python and PySpark to prepare, transform, and deliver data for analytics and machine learning use cases.
Evaluate business problems and determine the most appropriate modeling techniques based on data characteristics, performance goals, and operational constraints.
Build and support production-ready machine learning systems and products that integrate forecasting and predictive capabilities into business processes.
Develop and maintain API-based services, including FastAPI solutions, to expose machine learning models for downstream applications.
Measure and improve model performance by tracking accuracy, validating outputs, and refining approaches to strengthen prediction quality.
Partner with technical and business stakeholders to define architecture standards for machine learning platforms and deployed data products.
Contribute to MLOps best practices, including versioning, automation, reproducibility, and operational support for deployed models.

Requirements

Extensive experience designing architecture for machine learning or data-driven platforms in production environments.
Strong programming skills in Python, including scripting for data processing, model development, and automation.
Hands-on expertise with PySpark for large-scale data transformation and pipeline development.
Solid understanding of machine learning algorithms and the ability to choose suitable models for forecasting, prediction, and applied data science problems.
Experience deploying machine learning models into operational systems and supporting them through the full lifecycle.
Familiarity with FastAPI or comparable frameworks used to serve machine learning functionality through APIs.
Knowledge of model evaluation methods, including approaches for assessing and improving accuracy and performance.
Background in machine learning engineering, data science, or machine learning architecture with a focus on building practical business solutions.

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