Technical Solutions Architect, Internal Enterprise Product Strategy

Google Google · Big Tech · Austin, TX +3

This role focuses on defining and scaling enterprise-wide architectural standards, reference models, and patterns for data and AI enablement within Corporate Engineering. The Technical Solutions Architect will author core reference architectures and blueprints to modernize the data lifecycle and accelerate the deployment of safe, scalable AI solutions, working with technical leads and engineering squads to advocate modern data architecture and drive adoption of enterprise AI standards.

What you'd actually do

  1. Author Core Blueprints and Standardize AI Patterns.
  2. Drive Adoption and Cross-Pillar Enablement.
  3. Collaborate with Environmental Protection Agency Peers.
  4. Evaluate Industry Trends and Upskill Engineering Teams.

Skills

Required

  • technology
  • software engineering
  • enterprise architecture
  • data engineering
  • AI/ML systems
  • data architecture frameworks
  • enterprise data warehouses
  • data lakes
  • production-grade AI/ML pipelines
  • solution architectures
  • system design techniques
  • distributed systems

Nice to have

  • data product strategies
  • modern data tooling
  • cloud-native data services
  • machine learning operations frameworks
  • large language models (LLM) orchestration patterns
  • organizational skills
  • investigative skills
  • influencing skills

What the JD emphasized

  • 8 years of experience in technology, software engineering, or enterprise architecture, with an emphasis on data engineering or AI/ML systems.
  • Experience designing and implementing data architecture frameworks, enterprise data warehouses/data lakes, or production-grade AI/ML pipelines.
  • Knowledge of modern data tooling, cloud-native data services, machine learning operations frameworks, and large language models (LLM) orchestration patterns.

Other signals

  • enterprise-wide architectural standards
  • reference models
  • patterns
  • modernize our data life-cycle
  • accelerate the deployment of safe, scalable AI solutions
  • advocate modern data architecture
  • unblock complex technical hurdles
  • drive the active adoption of enterprise AI standards