Job Summary
This role is responsible for defining and executing the multi‑year architectural and engineering vision for Enterprise AI platforms at scale. The position drives the adoption of next‑generation AI technologies, including Agentic AI and neuro‑symbolic systems, to enable business‑critical innovation.
As a senior engineering leader, you will scale and shape large, multi‑layered engineering organizations, establish reusable platforms, and ensure that AI solutions are secure, governed, and production‑ready. You will work closely with business and technology stakeholders to ensure that AI investments translate directly into measurable business outcomes.
This is a highly impactful role for leaders passionate about building AI‑first platforms, influencing enterprise‑wide transformation, and shaping the future of applied AI at scale.
Key Responsibilities
| Technical Vision: Define the multi-year architectural vision for Enterprise AI, driving the adoption of next-gen technologies like Agentic AI and Neuro-symbolic systems. |
| Organizational Scale: Scale the engineering organization to support rapid growth, establishing best practices for cross-team collaboration and platform reusability. |
| Strategic Investments: Make high-judgment decisions on technology investments, such as selecting foundation models or standardizing on specific vector databases. |
| Governance & Security: Lead the implementation of AI Governance and Security frameworks (RBAC, PII redaction) across all engineering teams. |
| Leadership Pipeline: Develop a robust pipeline of engineering leaders (Managers and Principal Engineers), mentoring them on complex system design and organizational leadership. |
| Business Alignment: Align technical execution with business goals, ensuring that engineering output directly drives revenue and customer adoption. |
Skill Requirements
Experience: Extensive experience leading large engineering organizations (Managers of Managers) in the Data/AI space.
Architecture: Mastery of enterprise architecture patterns for Big Data and AI.
Strategy: Proven ability to define long-term technical roadmaps and drive organizational transformation.
Domain: Deep understanding of the AI ecosystem (Hugging Face, OpenAI, Hyperscalers).
Operational: Experience managing large budgets (cloud spend, headcount) and vendor relationships.
Other Requirements
Experience leading the engineering integration of M&A targets.
Experience building "AI-first" products from the ground up.
Public speaking experience at major technical conferences (e.g., KubeCon, NeurIPS Industry tracks).
Used Technologies, Services, Languages, or Frameworks:
Domain: Foundation Models, Agentic Workflows, Sovereign AI.
Architecture: Event-Driven Architecture, Data Mesh.
Management: Org Design, Technical Strategy.