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

What You'll Do 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. Key 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 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 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 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 inference AWS 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 Information Salary ranges below reflect targeted base salaries. Non-sales positions have the opportunity to participate in a bonus program. Sales positions are eligible for sales incentives, and in some instances a bonus plan, whereby total compensation may far exceed base salary depending on individual performance. Actual compensation for all roles will be based upon geographic location, work experience, education, licensure requirements and/or skill level and will be finalized at the time of offer. Salary Range (Non-Exempt expressed as hourly; Exempt expressed as yearly) $127000 - $191000 / year Time Off Program Flexible Time Off (FTO) is provided to salaried (exempt) employees and provides the opportunity to take time away from the office with pay for vacation, personal or short-term illness. Employees don’t accrue a bank of time off under FTO and there is no set number of days provided. Pension Eligible Yes Job level We’ll consider talent at the next level with the right experiences and skills. Work Environments This role offers in-office, hybrid (blending at least three office days in a typical workweek). You’ll work with your leader to figure out which option may align best based on several factors. Work Authorization/Sponsorship At this time, we're not considering candidates that need any type of immigration sponsorship (additional work authorization or permanent work authorization) now or in the future to work in the United States. This includes, but IS NOT LIMITED TO: F1-OPT, F1-CPT, H-1B, TN, L-1, J-1, etc. For additional information around work authorization needs please use the following links. Nonimmigrant Workers and Green Card for Employment-Based Immigrants Investment Code of Ethics For Principal Global Investors positions, you’ll need to follow an Investment Code of Ethics related to personal and business conduct as well as personal trading activities for you and members of your household. These same requirements may also apply to other positions across the organization. Experience Principal While our expertise spans the globe, we're bound by one common purpose: to foster a world where financial security is accessible to all. And our success depends on the unique experiences, backgrounds, and talents of our employees – individually and all of us together. Explore our core values, benefits and why we’re an exceptional place to grow your career. Principal is an Equal Opportunity Employer All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran stat Posting Window We will accept applications for 3 full days following the Original Posting Date, after which the posting may remain open or be removed based upon applications received. If we choose to post the job again, we will accept additional applications for at least 1 full day following the Most Recently Posted Date. Please submit applications in a timely manner as there is no guarantee the posting will be available beyond the applicable deadline. Original Posting Date 9/28/2026 Most Recently Posted Date 9/28/2026 Principal uses artificial intelligence tools to assist in reviewing and evaluating job applications, fraud prevention, and candidate matching and comparisons. These AI tools support our human recruiters in the initial review process but do not make final hiring decisions without human involvement. By submitting your application, you acknowledge this use of AI in our recruitment process. Please review our Workforce (U.S.) Privacy Notice for more details on our practices and your data privacy rights.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. Key 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 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!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 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 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 inference AWS 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)