Principal Digital Architect

Caterpillar · Industrial · Chicago, IL +1

This Principal Digital Architect role focuses on defining and governing the architecture of large-scale digital platforms, with a specific emphasis on AI systems. The role involves owning end-to-end architecture solutions, ensuring scalability, performance, security, and rapid delivery, while influencing enterprise technology strategy. Key responsibilities include creating AI reference architectures, assessing AI technologies, and guiding teams in taking AI systems from proof-of-concept to scaled production use, balancing classical ML, LLM-based approaches, and non-AI solutions.

What you'd actually do

  1. Own and define solution and platform architectures for large‑scale, distributed systems from concept through production.
  2. Create architecture that meets high standards for scalability, performance, resilience, and security.
  3. Partner closely with business leaders, product owners, engineering managers, and delivery teams to ensure architectural alignment with business outcomes.
  4. Assess, select, and introduce new technologies, including proof‑of‑concept development and architectural spikes.
  5. Ensure solutions meet security, compliance, and regulatory requirements.

Skills

Required

  • Architectural Thinking
  • Technical Leadership
  • Communication
  • Requirements Analysis
  • Platform & Application Architecture
  • Python
  • Java
  • AWS services
  • containerization
  • orchestration (Docker, Kubernetes)
  • SQL and NoSQL databases
  • data warehouses (Snowflake)
  • data modeling
  • replication
  • sharding
  • DevOps practices: CI/CD
  • infrastructure-as-code
  • observability
  • automated testing
  • API design experience (REST, GraphQL, gRPC)

Nice to have

  • Experience defining AI reference architectures and standards for enterprise adoption
  • Ability to explain and defend architectural trade-offs between classical ML, LLM-based approaches, and non-AI solutions
  • Proven experience taking AI systems from proof-of-concept to scaled production use
  • Deep understanding of data architecture
  • Experience with modern DevOps practices
  • Strong API design experience

What the JD emphasized

  • Experience defining AI reference architectures and standards for enterprise adoption
  • Ability to explain and defend architectural trade-offs between classical ML, LLM-based approaches, and non-AI solutions
  • Proven experience taking AI systems from proof-of-concept to scaled production use

Other signals

  • Defining AI reference architectures and standards for enterprise adoption
  • Experience taking AI systems from proof-of-concept to scaled production use
  • Ability to explain and defend architectural trade-offs between classical ML, LLM-based approaches, and non-AI solutions