Lead Architect – Full-Stack Cloud Data & AI Engineering

It's fun to work in a company where people truly BELIEVE in what they are doing! _We're committed to bringing passion and customer focus to the business._ **Lead Architect – Full-Stack, Cloud, Data & AI Engineering** _Technical leadership of the end-to-end build, with accountability for establishing the team's deployment capability and mentoring Forward Deployed Engineers to independence_ **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. **Capability Coverage** ======================= **Full-stack engineering** **What the role is accountable for -** Standards and patterns for Python services, JavaScript/TypeScript front ends, SQL and NoSQL data design, APIs, CI/CD and DevOps **Mode of working -** Guide, review, spike **Azure cloud architecture** **What the role is accountable for -** Target-state architecture, service selection, identity, networking, environments, non-functional targets and cloud cost discipline **Mode of working -** Own and decide **Data engineering** **What the role is accountable for -** PySpark and Databricks pipeline architecture, layered data design, quality controls and performance standards **Mode of working -** Direct and review **AI engineering & AIOps** **What the role is accountable for -** Agent and orchestration design, evaluation harnesses, guardrails, human-approval flows, tracing, versioning and drift monitoring **Mode of working -** Own and direct **Leadership 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. **Required Experience** ======================= * 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. **Success Measures** ==================== * Named FDEs deploy and operate solutions independently; delivery is not dependent on this individual. * Time-to-deploy reduces engagement over engagement through reusable assets and standards. * Solutions reach production on committed timelines, with architecture and security accepted with minimal remediation. * Agent quality, availability, latency and cloud cost tracked against defined baselines, with regressions caught pre-release. If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us! Not the right fit?  Let us know you're interested in a future opportunity by clicking _Introduce Yourself_ in the top-right corner of the page or create an account to set up email alerts as new job postings become available that meet your interest!