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

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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.

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!

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