Machine Learning Engineer

FULL-TIME Position.

Machine Learning & Model Deployment

• Design, develop, train, deploy, and support machine learning models using Python, SQL, Azure Machine Learning, AutoML.
• Collaborate with data engineers and analysts to define data requirements and ensure data readiness.
• Build, test, and monitor predictive models for performance and accuracy.
• Automate model training, scoring, and deployment workflows using Azure DevOps and ML pipelines.
• Document experiments, model parameters, and deployment procedures.

Data Engineering & Pipeline Development

• Develop and maintain ETL/ELT pipelines using Azure Data Factory, Synapse, and Python.
• Manage and optimize relational and cloud-based data stores for analytics and ML workloads.
• Implement data quality, governance, and security practices throughout the model lifecycle.
• Support feature engineering, data preprocessing, and scalable data architecture.
• Collaborate with engineering teams to ensure integration of ML outputs into production systems.

Azure Cloud Infrastructure & Automation

• Leverage Azure services (Functions, Logic Apps, Event Hubs) for event-driven and automated data processing.
• Utilize CI/CD pipelines for automated model deployment, versioning, and rollback.
• Ensure compliance with security and governance standards across all data and ML assets.
• Optimize cost and performance of Azure ML and data resources.

Collaboration & Communication

• Work cross-functionally with product, engineering, and risk teams to deliver data-driven solutions.
• Translate analytical and technical results into actionable insights for business stakeholders.
• Maintain clear documentation of workflows, architecture, and operational procedures.

SKILLS AND EXPERIENCE

• Bachelor s or Master s degree in Data Science, Computer Science, or a related field.
• 5 7 years of experience in data engineering, machine learning, or related roles.
• Proficiency in Python, SQL, and Azure services (Azure ML, Synapse, Data Factory, Data Lake).
• Experience deploying ML models into production environments.
• Strong understanding of statistics, data modeling, and MLOps best practices.
• Excellent problem-solving and communication skills.

PREFERRED QUALIFICATIONS

• Experience with Azure DevOps, GitHub Actions, or similar CI/CD tools.
• Professional Machine Learning Engineer (PMLE) Certification
• AWS Certified Machine Learning Engineer (MLA-C01)
• Knowledge of data governance, SOC 2 compliance, and ML model monitoring.
• Exposure to financial services, credit union, or banking data environments.
• Familiarity with Power BI or similar visualization tools.