Principal Data Engineer - AI Architect

American Family Insurance Group-posted about 1 month ago
Full-time • Senior
Hybrid • Boston, MA
5,001-10,000 employees
Insurance Carriers and Related Activities
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As the Principal Data Engineer - AI Architect you will lead the architectural design (conceptual, logical and physical) of complex, large-scale AI business solutions. As a technical lead, you will guide and align development efforts across multiple teams to ensure cohesive and delivery. You will report to the Director, Data Science. #LI-Hybrid.

  • Lead the architectural design of scalable, secure, and resilient AI systems, ensuring integration with core business platforms including policy administration, customer relationship management (CRM), and claims management systems.
  • Design and implement end-to-end operational patterns for AI solutions, including real-time (e.g., API-driven), pub/sub, and asynchronous batch processing architectures. This sets the standards for how systems interact with AI capabilities.
  • Develop strategies for using diverse data sources, from direct application integrations to large-scale analytical data in our data and object lakes, to fuel AI models and applications.
  • Provide subject matter expertise on AI system architecture, security, and infrastructure, overseeing project implementation to ensure successful, production-grade solution delivery.
  • Establish governance processes for model life cycle management and governance processes - including, architecture reviews, application and model registries, versioning.
  • Define and promote the strategic direction for the AI engineering function, creating and maintaining a technology roadmap that incorporates latest trends in AI and cloud computing services.
  • Be the senior escalation point for assessing complex issues within AI systems, assessing scope, urgency, and potential growth.
  • Establish and champions engineering best practices, frameworks, and reusable patterns for AI development to enhance velocity and quality across teams.
  • Demonstrated experience providing customer-driven solutions, support or service.
  • Demonstrated experience leading software engineering architectures, system/software designs, and system deployments.
  • Extensive knowledge and understanding of software engineering technologies, enterprise level network architecture, and application development methodologies.
  • Demonstrated experience developing solution-delivery and design approaches and solutions.
  • Demonstrated experience developing complex software/systems using multiple programming languages.
  • Extensive knowledge and understanding of the systems development life cycle (SDLC).
  • Extensive knowledge and understanding of infrastructure technologies, operating systems, and the interconnectivity between platforms and software tools.
  • Extensive knowledge and understanding of integration and migration strategies and technologies.
  • Demonstrated experience providing technical guidance and leadership to less experienced staff.
  • Experience architecting and deploying complex, software solutions, with a strong emphasis on AI/ML-powered applications.
  • Knowledge of AI/ML system design, MLOps principles, and modern application development methodologies. This includes frameworks and tooling for observability and monitoring.
  • Hands-on experience with Google Cloud Platform (GCP), including deep familiarity with services such as Vertex AI, Cloud Run, Google Kubernetes Engine (GKE), BigQuery, Cloud Storage (GCS), and IAM for building and securing solutions.
  • Experience designing and implementing integration strategies for deploying AI models into production environments and core business systems.
  • Experience with traditional machine learning and statistical methods, as well as the infrastructure required to support them.
  • Demonstrated experience with designing scalable Generative AI solutions, including knowledge of concepts such as retrieval-augmented generation (RAG), model context protocol (MCP), prompt management, evaluation frameworks, and AI safety guardrails.
  • Extensive knowledge of CI/CD for both software and machine learning models. This includes aspects of versioning, change management, monitoring, and visibility.
  • Demonstrated experience providing technical guidance and mentorship to engineering staff.
  • comprehensive medical, dental, vision and wellbeing benefits
  • competitive 401(k) contribution
  • pension plan
  • annual incentive
  • 9 paid holidays
  • paid time off program (23 days accrued annually for full-time employees)
  • student loan repayment program
  • paid-family leave
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