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

Fractal; Mumbai; On-site; INR 3,500,000 - 5,000,000; Full time

Job summary

Fractal is seeking a Lead Architect to set and own the technical direction across application, cloud, data and AI layers. The role emphasizes raising engineering capability, establishing standards, and mentoring engineers until they can deploy and operate solutions in client environments without escalation.

Hands-on work is selective; the focus is on credible guidance, reusable assets, and scalable deployment practices.

Qualifications

• 10+ years in software or applied AI engineering with leadership experience.
• Full-stack delivery background including Python, SQL/NoSQL and web deployment.
• Hands-on architecture with client cloud and security teams.

Responsibilities

• Define and own the target architecture for enterprise AI solutions.
• Lead through the team with design walkthroughs, reviews, and standards.
• Build reusable deployment assets, IaC modules, pipelines and runbooks.
• Mentor Forward Deployed Engineers to independence and deployment ownership.
• Ensure architecture and security posture is maintained during client engagements.
• Prototype high-risk components to unblock delivery when needed.

Skills

Leadership

Full-stack engineering

Azure cloud

Data engineering

AI engineering

Mentoring

DevOps

Tools

Databricks

PySpark

IaC (Infrastructure as Code)

Job description

We're committed to bringing passion and customer focus to the business.

Capability Coverage

It's fun to work in a company where people truly BELIEVE in what they are doing!

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!