Principal Solution Architect, AI Co-innovation

Cognite Cognite · Industrial · United States · Strategic Projects

This role is for a Principal Solution Architect focused on co-innovating and scaling AI solutions within industrial digitalization. The primary responsibility is to ensure the architectural integrity and strategic success of AI deployments, particularly Cognite AI Agents and custom front-end applications. The role involves designing complex industrial data models, architecting multi-agent systems with LLMs and tool use, and collaborating with customers to integrate solutions into production. The goal is to establish repeatable patterns for enterprise-scale AI impact and feed customer feedback into the product roadmap.

What you'd actually do

  1. Set and maintain the standards for data model quality and system architecture across strategic accounts to ensure reliable, production-grade AI solutions.
  2. Design and implement sophisticated industrial data models within Cognite Data Fusion (CDF), providing the necessary "ground truth" for LLMs and agentic workflows.
  3. Architect multi-agent systems that combine LLMs, tool use, evaluation loops, and reasoning over industrial data.
  4. Collaborate with technical teams on-site to refine use cases and integrate solutions into production environments.
  5. Identify and document architectural patterns that reduce delivery friction and enable the team to move from bespoke prototypes to repeatable, enterprise-scale impact.

Skills

Required

  • 10+ years of experience in software engineering or solution architecture
  • Hands-on experience with LLMs, embeddings, prompt engineering
  • Proven track record of designing and shipping enterprise-scale software architectures
  • Strong storytelling skills for both technical and executive audiences
  • Clean, maintainable, and scalable code with solid practices around testing, version control, and code review
  • Familiarity with modern AI development workflows, rapid iteration, and agentic engineering tools

Nice to have

  • Understanding of industrial data types (time series, knowledge graphs)
  • Experience with Cognite Data Fusion
  • Familiarity with vector databases and RAG architectures
  • Cloud-native development experience (AWS, Azure, GCP)
  • Exposure to industrial domains - manufacturing, energy, process industries
  • Experience with industrial control systems
  • Deep fluency with agentic coding tools and an understanding of what they mean for the future of software engineering - and industrial workflows

What the JD emphasized

  • architectural integrity
  • AI deployments
  • AI Agents
  • agentic systems
  • LLMs
  • tool use
  • evaluation loops
  • industrial data
  • enterprise-scale software architectures
  • AI solution architecture

Other signals

  • architectural integrity
  • AI deployments
  • AI Agents
  • agentic systems
  • LLMs
  • tool use
  • evaluation loops
  • industrial data