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

Fractal Analytics Inc.; India; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Fractal Analytics Inc. is seeking a Lead Architect – Full-Stack, Cloud, Data & AI Engineering to set the technical direction for enterprise AI solutions and mentor Forward Deployed Engineers to independence.

This role combines hands-on work with strategic leadership to raise engineering capability and establish reusable deployment assets. You will drive standards for Python services, frontend, data models, APIs, CI/CD, and cloud architecture across Azure, Databricks, and PySpark, with a focus on

Qualifications

• 10+ years in software, platform or applied AI engineering.
• 4+ years leading engineering teams on production systems.
• Proficient in Python, SQL/NoSQL, and web deployment with CI/CD and DevOps.
• Hands-on architecture experience with PySpark and Databricks; mentoring engineers.

Responsibilities

• Define and own the target architecture across apps, cloud, data and AI layers.
• Lead through the team: design reviews, standards, and enablement.
• Mentor Forward Deployed Engineers to independence.
• Ensure deployment assets, IaC modules and reusable templates are created.

Skills

Leadership

Full-stack delivery

PySpark

Databricks

Python

CI/CD

Tools

Orchestration framework

Job description

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

• 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

• 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

• Accountable for: PySpark and Databricks pipeline architecture, layered data design, quality controls and performance standards.
• Mode of working: Direct and review.

AI engineering & AIOps

• 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.
• Selectively 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!

At Fractal, towards our goal of “powering every human decision in the enterprise,” our partnerships and alliances help in creating and delivering a compelling suite of solutions to unlock value. We partner with companies from around the globe, leaders in their respective fields. With Fractal’s expertise in artificial intelligence, design, engineering, and digital transformation, combined with the data, technology, and software platforms from our partners, we create cutting‑edge solutions to problems in the business world. We understand how critical and timely decision triggers, and information, empower our clients to create, unlock, deliver, and realize value. Together with our partners, our goal is to serve each client in their end-to‑end data‑to‑decision journey.