Sr Software Engineer II (Team Leader, Model Operations & Enablement)

About the role

As a Sr Engineer II (Team Leader, Model Operations & Enablement), you will lead the team that builds and operates Principal's Enterprise AI pipeline platforms — the platform our builders rely on to deploy, monitor, and maintain AI models in production. You will work closely with data science, engineering, and business stakeholders across our company to standardize how models move from idea to production and stay healthy once they're there.

Responsibilities

• Lead the MOE team and grow its capabilities to own and design all layers of the model operations stack. Model training (AWS SageMaker, XGBoost, Scikit-Learn, PyTorch), CI/CD deployment, real-time and batch inference serving, and production monitoring
• Own and continually reduce the complexity of the enterprise AI pipeline, including AWS (Glue, S3, SageMaker, IAM), the Snowflake-based Enterprise Data Foundation (EDF), and model registry
• Set short- to medium-term strategic direction for the MOE platform (6–12 months out), including expanding automated model monitoring and drift detection, and extending LLMOps coverage (performance stability, latency, uptime, logging, security, explainability) as generative and agentic use cases come online
• Run recurring intake consultations and "office hours" with data science partners to scope new products and move the model backlog (value/expected-value models, retention and cross-sell likelihood models, claims monitoring, customer lifetime value, and similar) from consultation through operationalization
• Facilitate cross-team work between MOE and data science/analytics engineering partners, including migrating legacy models off ad hoc pipelines and onto the enterprise MLOps pipeline
• Own the change-management rigor (risk/impact analysis, migration plans) required to ship safely in a regulated insurance and financial services environment, including privacy impact assessments where third-party data is involved
• Build technical depth and a leadership bench on the team: give each team member the opportunity to lead or co-lead an ML project over the year, and foster a culture of accountability, coaching, and career development
• Create and deliver training on the MLOps pipeline, present MLOps progress and thought leadership at least quarterly, and measure pipeline usability directly with end users
• Provide technical guidance and mentorship

Qualifications

• Associate or bachelor's degree (preference in a computer science, technology, engineering, or math-related field) and 8+ years' work experience or equivalent experience
• 5+ years of experience working with AWS is required (Glue, S3, SageMaker, IAM), Snowflake, feature store and model registry concepts, and ML frameworks (XGBoost, Scikit-Learn, PyTorch)
• Formal Leadership experience is required (leading or managing a technical team, including coaching and career development)
• Proven experience as an IT professional, with demonstrated experience in MLOps, ML infrastructure, or similar model-operations work
• Experience standing up or operating model monitoring and drift detection in production
• Familiarity with agile methodologies

Skills