Senior Machine Learning Solutions Architect

Responsibilities

• Architect end to end machine learning solutions from data ingestion through production consumption across multiple business use cases
• Design and standardize how data flows across systems to support machine learning, analytics, personalization, and real time decisioning
• Define the architectural patterns that support feature engineering, model training workflows, experimentation, and model evaluation at scale
• Define MLOps patterns that enable consistent deployment, monitoring, and lifecycle management of models at scale
• Build reusable capabilities such as feature pipelines, model serving frameworks, and data access patterns
• Define how machine learning and analytical outputs are exposed through APIs, batch processes, and real time services
• Partner with data scientists and engineers to remove friction between experimentation and production while driving key architectural decisions

Requirements

• Bachelor's degree in Data Science , Statistics, Computer Science, or a closely related quantitative field
• 8 plus years of experience in data, platform, or software engineering roles with exposure to machine learning or advanced analytics
• Experience designing and delivering production grade machine learning or advanced analytics solutions
• Strong background in data architecture and data movement across distributed systems
• Deep understanding of machine learning workflows including feature engineering, model training, experimentation, evaluation, and production deployment
• Experience with modern data and machine learning platforms such as AWS, Snowflake, Databricks, or similar
• What You Will Set You Apart
• Experience designing systems that directly enable personalization, marketing activation, or customer level decisioning
• Experience building or scaling MLOps capabilities beyond experimentation into production use
• Experience working with real time or event driven data and machine learning use cases
• Experience working closely with data scientists to productionize models and scale experimentation into repeatable systems
• Proven ability to connect machine learning with broader analytics and business workflows
• Relevant certifications such as AWS Certified Solutions Architect, AWS Machine Learning Specialty, Snowflake SnowPro Advanced Data Engineer or Data Scientist, Databricks Machine Learning Professional, or Google Professional Machine Learning Engineer
• This job operates in a professional office environment.
• What we offer you
• We offer an array of diverse and inclusive benefits regardless of where you are in your career. We believe that pr

Benefits

Additional Information

Our vision for the future is based on the idea that transforming financial lives starts by giving our people the freedom to transform their own. We have a flexible work environment, and fluid career paths. We not only encourage but celebrate internal mobility. We also recognize the importance of purpose, well-being, and work-life balance. Within Empower and our communities, we work hard to create a welcoming and inclusive environment, and our associates dedicate thousands of hours to volunteering for causes that matter most to them. Chart your own path and grow your career while helping more customers achieve financial freedom. Empower Yourself. ***Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time, including CPT/OPT.*** As a Senior Machine Learning Solution Architect, you will shape how machine learning and advanced analytics are designed, delivered, and scaled across the organization. You will own the architectural patterns that move data from fragmented sources into production grade machine learning, analytics, and AI use cases that directly power business outcomes. This role sits at the intersection of data, machine learning, and application architecture, defining how data is structured, moved, and activated to enable personalization, marketing, reporting, and real time decisioning. This includes enabling feature engineering, model training pipelines, experimentation workflows, and model evaluation frameworks at scale. You will partner with data scientists, engineers, and product teams to turn modeling efforts into scalable production systems, focusing on solving real constraints and enabling consistent, reusable capabilities across the enterprise.

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