AI Architect

Dublin, United States | Posted on 01/07/2026

Infoverity is a leading systems integrator and global professional services firm driven to simplify and maximize the value of their clients’ information. Founded in 2011, Infoverity provides complementary services for many digital initiatives, including MDM and PIM Strategy and Implementation, Data Governance and Analytics, Content Management, Data Integration, Enterprise Hosting, and Operational Services that help large enterprises in the retail, consumer goods, manufacturing, financial and healthcare sectors simplify and maximize the value of their information.

Infoverity, a 100% employee-owned company, has been on the Inc. 5000, recognized by IDG’s Computerworld as one of the Best Places to Work in IT, as a Wonderful Workplace for Young Professionals and as a “Best Place to Work” by Inc. Magazine and Business First. Infoverity’s global headquarters is in Dublin, Ohio. The EMEA headquarters and Global Development Center is in Valencia, Spain.

Additional offices are located in Germany and India.

Infoverity is seeking a Data & AI Architect to design, build, and scale enterprise data and AI solutions. In this strategic role, you will bridge the gap between business objectives and technical execution, architecting scalable AI solutions that deliver measurable business value.

You will work across modern data platforms (Databricks, Snowflake, Microsoft Fabric etc.) and cloud ecosystems
, supporting the full lifecycle from data ingestion and modeling through AI enablement, agent deployment, and production operations
. This is a hands‑on, consultative role requiring strong architectural judgment, customer engagement skills, and the ability to guide solutions from proof‑of‑value to enterprise scale
.

• Define Data & AI Strategy: Partner with business and technical stakeholders to translate enterprise challenges into data, analytics, and AI solution architectures, producing clear roadmaps, reference architectures, and implementation plans.
• Enterprise Integration: Architect solutions that integrate with enterprise source systems, APIs, operational platforms, and downstream consuming layers.
• Standards & Governance: Establish design patterns and technical standards across data management, analytics, and AI—covering security, scalability, performance, lineage, and compliance requirements.

• Modern Data Stack Architecture: Design and implement scalable data pipelines, transformation layers, and orchestration frameworks across platforms such as Databricks, Snowflake, Microsoft Fabric, GCP or similar.
• Analytics & Semantic Layers: Support dimensional modeling, feature stores, and semantic layers to enable BI, advanced analytics, and AI workloads.
• Cloud Infrastructure: Design secure and best practice architectures on AWS, Azure or GCP
  , leveraging managed services, serverless patterns, and containerized workloads (Docker/Kubernetes) where appropriate.
• AI Enablement on Data Platforms: Enable AI and ML workloads using curated, governed enterprise data—supporting model training, inference, and retrieval‑based architectures (e.g., RAG).
• Agent‑Based Architectures: Design and deploy agentic or workflow‑driven AI solutions
  , incorporating orchestration, tool/function calling, guardrails, and observability to support multi‑step business processes.
• Prototyping to Production: Lead Proof‑of‑Concept and pilot initiatives, validating business value and technical feasibility before scaling to production‑grade solutions.
• Technical Leadership: Review solution designs, contribute to critical implementation components, and mentor engineers across data engineering, analytics, and AI best practices.
• Cross‑Team
  
  Collaboration:
  
  Partner with delivery leads and stakeholders to manage technical risks, trade‑offs, and dependencies throughout project life cycles.
• Operational Readiness: Support deployment, monitoring, and optimization of data and AI solutions to ensure reliability, performance, and long‑term maintainability.

• Professional
  
  Experience:
  
  …