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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-deter

📌 Lead Architect – Full-Stack, Cloud, Data (Mumbai)
🏢 Fractal
📍 Mumbai