This position serves as Armada's lead AI & Data engineer while developing talent in the Artificial Intelligence space. Responsible for owning the technical vision, architecture, engineering standards, and future-state capabilities that enable AI at enterprise scale.
This role is designed for a recognized technical leader who has successfully delivered AI, cloud, data, and software engineering solutions and is ready to own the strategic direction of Armada's AI engineering ecosystem. The Manager of AI and Data Engineering will lead the design and implementation of enterprise AI platforms, intelligent agents, generative AI solutions, advanced analytics architectures, and scalable AI engineering practices across the organization.
The ideal candidate brings deep expertise across Azure, Microsoft Fabric, Databricks, Snowflake, modern software engineering, cloud-native architectures, and emerging AI technologies. They possess the technical depth to solve complex engineering challenges and the leadership ability to influence technology strategy across business and IT stakeholders.
This role is expected to establish the engineering foundation that enables Armada to scale AI safely, efficiently, and responsibly while driving measurable business value.
PRIMARY RESPONSIBILITIES
Enterprise AI and Data Engineering
Establish technical standards, solution architectures, and engineering patterns for all AI initiatives.
Lead technology decisions involving AI platforms, data platforms, cloud infrastructure, and intelligent automation capabilities.
Evaluate emerging AI technologies and determine enterprise applicability.
Create scalable architectural patterns that balance innovation, maintainability, security, and cost optimization.
Serve as the coach for AI engineering across Armada.
Enterprise AI Platform Ownership
Design and evolve Armada's AI platform capabilities across Azure, Microsoft Fabric, Databricks, and Snowflake.
Establish platform strategies for Generative AI, RAG, machine learning, agent frameworks, vector search, and intelligent automation.
Define standards for AI lifecycle management, model deployment, monitoring, and observability.
Build reusable engineering capabilities that accelerate AI delivery across the enterprise.
Establish AI engineering guardrails, governance controls, and operational standards.
AI Application & Agent Development
Lead the design and implementation of enterprise AI agents and copilots.
