Principal AI Architect

Principal AI Architect

Ecolab Digital is seeking a commercial solutions focused Principal AI Architect to scale the value of AI/DS intelligence to deliver unique offerings for customers via digital software solutions with embedded AI. In this role, you will collaborate with the AI development teams, E2E solution architects, and technical engineering owners to design AI architecture within the larger solution architecture. You will own the strategic advancement of the AI tech stack and understand industry changes and opportunities to maximize solution design.

You will partner with AI development teams to review adherence and navigate new solution challenges through the development process. This role is aligned with a dynamic team at the forefront of technological innovation creating cutting-edge AI for intelligent solutions that transform how our customers operate for enhanced efficiency and sustainability.

Position Details

• Location:
  
  The desired location for this role is either Naperville, IL or St. Paul, MN with a hybrid schedule.
• Travel:
  Up to 10% of the time.

What You Will Do

• Define enterprise architecture across GenAI, agentic workflows, ML platforms, and data engineering ecosystems.
• Serve as a technical voice in leadership discussions, influencing strategy and navigating organizational challenges.
• Architect and implement scalable AI solutions including agentic ecosystems, advanced pipelines, and AI model integrations with larger end-to-end SAAS solutions.
• Evaluate and understand technologies that meet needs in support of cost optimized, multi-cloud solutions including aspects of Databricks, Snowflake, Azure and beyond.
• Elevate governance standards covering security, compliance, and responsible AI/data use.
• Mentor engineering teams, fostering autonomy, technical excellence, and architectural discipline.
• Evaluate emerging AI/data tooling and integrate reusable architectural patterns to accelerate delivery.
• Ensure alignment of architecture with business goals, scalability requirements, and innovation roadmaps.
• Provide clear, consistent communication and presentation to various stakeholders related to AI architecture strategy and implementation to drive alignment, education, and impact-based decisions.
• Work within an agile global resource model, planning for and delivering against initiatives.

Minimum Qualifications

• Bachelors Degree in Computer Science or related field and 10 years of experience in progressive architecture and AI/DS design and engineering
• Enterprise Architecture: AI + data ecosystem design, multi-agent orchestration (Lang Chain, Lang Graph, Haystack), enterprise-wide AI/ML standards.
• AI/ML Platforms: LLM APIs (OpenAI, Hugging Face, Anthropic, MosaicML), transformer architectures, RAG workflows, LLMOps frameworks (prompt lifecycle, monitoring).
• LLMOps frameworks (prompt lifecycle, monitoring, context engineering, harness engineering).
• Data & Cloud Infrastructure:
  Databricks (Delta Lake, Spark SQL, PySpark, MLflow), Snowflake (enterprise-scale warehousing), Azure (AKS, Synapse, Event Hub, Logic Apps).
• Programming & Integration:
  Python (Transformers, Lang Chain, PyTorch), API integration (REST, GraphQL, gRPC), microservices/event-driven systems.
• Governance & Security:
  Responsible AI frameworks, privacy/compliance, OAuth2, JWT, TLS, RBAC.
• Dev Ops & Delivery: CI/CD (Azure Dev Ops, Git Hub Actions), containerization (Docker, Kubernetes), observability (Prometheus, Grafana, ELK stack).
• Leadership & Strategy:
  Influence technical strategy, mentor engineers, simplify complex concepts for diverse stakeholders.
• Ability to thrive in an ambiguous environment, embracing change and utilizing proven reasoning in balancing practical business needs and architectural rigor
• No immigration sponsorship is available for this role at this time.

Preferred Qualifications

• Exposure to multi-cloud architectures (Azure, AWS, GCP).
• Strong understanding of token-efficient AI consumption with an ability to surface Fin Ops tradeoffs
• Experience with data mesh and federated AI systems.
• Familiarity with knowledge graphs and semantic search.
• Hands-on with MLOps orchestration tools (Kubeflow, MLflow, Vertex AI).
• Understanding of edge AI deployment and IoT integration.
• Experience in enterprise architecture frameworks (TOGAF, Zachman).
• Knowledge of AI ethics frameworks and regulatory compliance (EU AI Act, ISO standards).
• Demonstrated interconnected disciplinary knowledge of digital solution development