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Open nowPosted 62 days ago

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

Fractal110 open roles

Where
Mumbai
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Your applicationOpen nowLead Architect – Full-Stack, Cloud, Data & AI EngineeringFractal · Mumbai

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. Fractal postings stay open a median of 33 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 62 days ago

Fractal median: 33 days open

The posting

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!

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