Machine Learning Engineer

Job Summary

We are seeking a Machine Learning Engineer with strong expertise in machine learning model development, data engineering, and modern cloud-based analytics platforms. This role will focus on building ML-ready data architectures, developing scalable machine learning solutions, and supporting enterprise analytics initiatives. The ideal candidate will possess hands-on experience with Azure Databricks, Python-based model development, Medallion Architecture, and MLOps practices, along with the ability to collaborate effectively with business and technical stakeholders.

Key Responsibilities

• Design, develop, and maintain machine learning solutions that support advanced analytics and predictive modeling initiatives.
• Build and optimize ML-ready data pipelines and data architectures using Medallion Architecture principles.
• Develop and manage data ingestion, transformation, and curation processes across Bronze, Silver, and Gold data layers.
• Create scalable feature engineering workflows and production-grade machine learning assets.
• Design and implement machine learning pipelines using Azure Databricks and related cloud technologies.
• Leverage Delta Lake, MLflow, and workflow orchestration tools to operationalize machine learning models and data transformations.
• Develop and maintain Python-based machine learning models, feature engineering processes, and MLOps automation solutions.
• Build and optimize SQL transformations, views, and ELT pipelines to support analytics and machine learning workloads.
• Design and maintain feature stores, semantic layers, and curated datasets that support enterprise reporting and machine learning initiatives.
• Integrate machine learning outputs into analytics platforms, dashboards, and business intelligence solutions.
• Collaborate with business stakeholders, technical teams, and leadership to translate business requirements into scalable data and machine learning solutions.
• Establish engineering standards, best practices, and scalable development processes for machine learning and data engineering initiatives.
• Monitor data quality, model performance, and operational effectiveness of machine learning solutions.

Required Qualifications

• 5-7 years of hands-on experience in machine learning engineering and data engineering.
• 10+ years of experience delivering enterprise-scale data, analytics, and machine learning solutions.
• Strong experience building machine learning models and supporting model development using Python.
• Extensive experience with Azure Databricks for machine learning, feature engineering, and data engineering workloads.
• Deep understanding of Medallion Architecture, including Bronze, Silver, and Gold data layer design and implementation.
• Experience designing ML-ready data architectures and scalable data engineering solutions.
• Experience migrating workloads to Databricks and implementing modern data platform architectures.
• Hands-on experience with Delta Lake, MLflow, and Databricks Workflows.
• Strong proficiency in Python for model development, feature engineering, and MLOps automation.
• Advanced SQL skills with experience building optimized transformations, views, and ELT pipelines.
• Experience designing feature stores, semantic models, and machine learning-ready datasets.
• Strong understanding of machine learning lifecycle management, data engineering best practices, and scalable architecture patterns.
• Ability to lead technical initiatives and establish engineering standards and development practices.
• Strong business acumen and ability to communicate effectively with technical and business stakeholders.
• Experience working in collaborative, fast-paced environments that encourage experimentation and innovation.

Preferred Qualifications

• Experience working within Microsoft Azure cloud environments.
• Experience integrating machine learning outputs into analytics platforms and business intelligence solutions.
• Experience designing dashboards and reporting solutions that surface machine learning insights, data quality metrics, and model performance indicators.
• Familiarity with Power BI, including DAX, semantic modeling, and visualization best practices.
• Experience supporting enterprise-scale analytics, data science, and AI initiatives.
• Experience mentoring technical teams and providing technical leadership on machine learning and data engineering projects.