Machine Learning Engineer

This is a FULL TIME POSITION. THE ROLE We are seeking a highly skilled and motivated Machine Learning Engineer to join our growing data and analytics team. This hybrid role blends data science and data engineering, focusing on building, deploying, and maintaining machine learning models and the data pipelines that power them. You will leverage Azure environments and data services to design and operationalize models that deliver insights, automation, and models to help our customers. This role is ideal for someone with experience in both data engineering and applied machine learning, who can bridge the gap between analytics and production systems. KEY RESPONSIBILITIES 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.

Created: 2026-03-04