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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! India: HiringsupportIndia@fractal.ai Outside India: HiringsupportROW@fractal.ai This inbox does not process resume submissions. All applications must be made through posted job openings 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 Fractals expertise in artificial .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.