Principal Data Engineer - AI Architect

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.

Position Compensation Range: $ - $

Pay Rate Type: Salary

Compensation may vary based on the job level and your geographic work location. Relocation support is offered for eligible candidates.

• 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),
  Big Query
  , 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…