Sr Software Engineer II; Team Leader, Model Operations & Enablement

Position: Sr Software Engineer II (Team Leader, Model Operations & Enablement)
What You'll DoAs 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.

Key Responsibilities:

Lead the MOE team and grow its capabilities to own and design all layers of the model operations stack. Model training (AWS Sage Maker, 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, Sage Maker, 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

Operating at the intersection of financial services and technology, Principal builds financial tools that help our customers live better lives. We take pride in being a purpose-led firm, motivated by our mission to make financial security accessible to all. Our mission, integrity, and customer focus have made us a trusted leader for more than 140 years.

As Principal continues to modernize its systems, this role will offer you an exciting opportunity to build solutions that will directly impact our long-term strategy and tech stack, all while ensuring that our products are robust, scalable, and secure!

Who You Are We are looking for someone with:

Associate or bachelor's degree (preference in a computer science, technology, engineering, or math-related field) and 8+ years' work experience or equivalent experience5+ years of experience working with AWS is required (Glue, S3, Sage Maker, 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 leading or managing a technical team, including coaching and career development

Experience standing up or operating model monitoring and drift detection in production

Familiarity with agile methodologies

Skills That Will Help You Stand Out Solid understanding of CI/CD deployment patterns for real-time and batch inferenceAWS Certified AI Practitioner or higher-level AWS certification

Experience with LLMOps practices (bias/toxicity drift, latency, explainability monitoring) for generative or agentic model use cases

Experience running intake/consultation processes to scope ML products with business stakeholders

Strong presentation skills and a track record of communicating technical work to non-technical audiences

Basic knowledge of insurance and financial services products (e.g., retirement/income solutions, group benefits, individual life)
Salary Range…