Fullstack Architect - AI Automation

Responsibilities:

• Own end-to-end architecture for new solutions' data model, services, integrations, UI, AI layer, deployment, and observability.
• Hands-on build of core modules, especially LLM integration, agentic orchestration, workflow engines, and anything novel the team has not built before.
• Lead technical discovery with customers, run whiteboarding sessions, size integrations, and produce target-state architecture decks.
• Deliver technical demos to customers and partners TimeCraft, Fingertips, document tampering detection, binder reconciliation, Kissflow flows, and any new product we launch.
• Run code review, engineering standards, and tech mentorship across the BU's engineers (junior to senior).
• Build reusable skills, prompts, Claude Code plugins, and internal tools that multiply the team's output.
• Own integrations with enterprise platforms Kissflow, Zoho, Salesforce, QuickBooks, SAP, NetSuite, Microsoft Dynamics 365 Power Platform and standardise the integration layer.
• Partner with the BU leader on the technical roadmap: what to build in-house vs. buy, what to productise, and when to deprecate.
• Operate deployed apps on the Pierian App Store (Node/React, PM2/Nginx, centralised SSO) and participate in incident response and post-mortems.
• Contribute to proposals and RFP responses; as the technical authority, you should be able to write the solution architecture section of a 30-page RFP in a few hours.

Requirements:

• 9-11 years of engineering experience, with at least 3 years as a hands-on architect or tech lead on production AI or automation systems.
• Full-stack depth: React/Next.js + Node.js/Express on the front; Python (FastAPI, Pandas, LangChain/LlamaIndex) on the back; SQL + at least one NoSQL store; and one cloud (Azure preferred, AWS acceptable).
• Practical experience shipping LLM-powered features into production, including prompt engineering, RAG pipelines, function/tool calling, evaluation harnesses, and cost/latency trade-offs.
• Hands-on, daily usage of AI coding tools Claude Code, GitHub Copilot, or Cursor with shippable output. You should be able to show a public or internal repo where you used these tools to build something real.
• Strong fundamentals: data structures, system design, API design, authentication (OAuth/SSO/SAML), async processing, and observability.
• Excellent written and spoken communication: able to explain a design to a junior engineer, a CFO, and a vendor partner, all in the same afternoon.
• Disciplined engineering habits, source control hygiene, PR reviews, docs that stay current, and tests that actually run in CI.