Sr Software Engineer II (Team Leader, Model Operations & Enablement)
or drop it. Work Authorization/Sponsorship is maybe minor metadata but could be kept as a paragraph. However they said remove boilerplate; this is not boilerplate but a policy. Might keep as a paragraph under a section? There's no heading for it; we could just include a
after other sections. But we need to use headings only for sections with real content: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We have responsibilities (What You'll Do). We have qualifications (Who You Are). We have skills (Skills That Will Help You Stand Out). We have Pay (Salary Range). We have Benefits (Time Off Program, Pension Eligible). We have Schedule? Work Environments (in-office, hybrid) could be Schedule? Actually schedule is about work arrangement; could be considered Schedule. We'll make a Schedule section for work environment. Benefits section includes Time Off Program and Pension Eligible. Also maybe we could include Work Authorization as a minor metadata paragraph after sections. We must not repeat job title as heading. So no heading for Sr Engineer II. We must not include duplicated lines. Ensure we preserve all substantive info. Let's extract sections: Responsibilities: bullet list from "Key Responsibilities:" and subsequent paragraphs. The responsibilities include many sentences; we can break into list items. Responsibilities list: - 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 Also there is a paragraph after responsibilities: "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!" This is more about company; could be placed under "About the role" or maybe a separate section "About the company". But we only have allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We could put that under "About the role". Good. Qualifications/Requirements: "Who You Are" includes 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 (duplicate); Experience standing up or operating model monitoring and drift detection in production; Familiarity with agile methodologies. We can combine into Requirements (or Qualifications). Since we have both headings allowed, we can choose Requirements for hard requirements and Qualifications for preferred? But we can just use Requirements for all. However we also have "Skills That Will Help You Stand Out" which is separate Skills section. Thus: Requirements: list items from Who You Are. Skills: list items from Skills That Will Help You Stand Out. Pay: Salary Range: $127000 - $191000 / year (Exempt). Also mention Non-sales positions have opportunity to participate in a bonus program. Could include that. Benefits: Time Off Program (Flexible Time Off (FTO) provided to salaried (exempt) employees...), Pension Eligible Yes. Schedule: 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. We might also include Work Authorization as a short paragraph after sections (maybe under a generic "Additional Information" but not a heading). Since we shouldn't create headings without real content, we can just add a
after the sections. We must not include boilerplate like Equal Opportunity Employer, Posting Window, Original Posting Date, AI tools notice, Investment Code of Ethics, Experience Principal, etc. Remove those. Now produce HTML fragment: use
for each section heading. Use - for lists. Use
for prose.
Structure:
About the role
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!
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
Requirements
- 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
- 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)
Pay
Salary range: $127,000 – $191,000 per year (exempt). Non‑sales positions are eligible to participate in a bonus program.
Benefits
- Flexible Time Off (FTO) provided to salaried (
for prose. Structure:
About the role
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!
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
Requirements
- 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
- 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)
Pay
Salary range: $127,000 – $191,000 per year (exempt). Non‑sales positions are eligible to participate in a bonus program.
Benefits
- Flexible Time Off (FTO) provided to salaried (