Lead Architect – Full-Stack, Cloud, Data & AI Engineering

Role Overview: The Lead Architect sets and owns the technical direction for enterprise agentic AI solutions across application, cloud, data and AI layers — and delivers it through the team rather than personally. The primary mandate is to raise engineering capability: establish standards and reusable deployment assets, guide design and review work, and mentor Forward Deployed Engineers until they can build, deploy and operate solutions in client environments without escalation. Hands-on work is expected selectively — to stay technically credible and unblock the team — not as sustained feature delivery.

Responsibilities

Technical direction: Own the target architecture and the agentic-versus-deterministic decisions; hold the line on where agents add value and where rules or workflows suffice; Lead through the team: Break scope into buildable increments, run design walkthroughs and code reviews, and set the coding, testing, release and documentation standards the team works to; Build deployment capability: Convert today's person-dependent deployment into documented, reusable practice — reference architecture, IaC modules, pipeline templates, runbooks and environment checklists; Mentor FDEs to independence: Pair on builds, review their designs, run structured enablement, and hand over deployment ownership against defined competency milestones; Stakeholder ownership: Carry architecture and security posture through client technology and security review; act as final technical escalation on deployment and production issues; Selective hands-on: Prototype high-risk components, resolve critical-path blockers, and review production code — sufficient depth to make credible decisions, without becoming the delivery bottleneck.

Requirements

10+ years in software, platform or applied AI engineering, including 4+ years leading engineering teams on systems that reached production; Full-stack delivery background — Python, relational and NoSQL stores, web application deployment, CI/CD and DevOps practice; Hands-on architecture experience with the standing to own and defend decisions with client cloud and security teams; Working depth in PySpark and Databricks, and in agent development with a mainstream orchestration framework plus evaluation and production monitoring; Demonstrated record of mentoring engineers and raising team capability — not only shipping personally.