Forward Deployed Engineer

About the job

DivyaSree Group — AI Engineering Roles

DivyaSree Group is a diversified, multi-business organisation with operations spanning real estate development, commercial and residential assets, hospitality, co-living, asset management, facilities and workplace services, and education through REVA University.The Group's other business verticals operate across three states, while its co-living business has a presence across seven states.

This multi-business, multi-location footprint creates a wide range of operational and customer-centric challenges across assets, services, communities, institutions, and enterprise functions. Group-wide Centres of Excellence (COEs) support these businesses by developing shared capabilities, platforms, standards, and specialist expertise.

As DivyaSree continues to expand, it is investing in AI, data, automation, and digital engineering to improve decision-making, operational efficiency, resident, guest, customer, and student experiences, asset performance, and enterprise scalability.We are expanding our multidisciplinary engineering team and hiring across AI, machine learning, data, software, analytics, automation, and digital product roles at multiple experience levels. The team will work across business units and COEs to build reusable platforms and intelligent systems that address complex, real-world problems at scale.

Forward Deployed Engineer — AI Initiatives

Location: Bengaluru

Applications open until: end of September, or as soon as we find the right candidates, whichever comes sooner. Early submissions get reviewed first, so don't wait for the deadline.

This is modeled on the Forward Deployed Engineer role. You don't belong to one business unit and could get deployed wherever in the Group there's a real problem worth solving with AI. It could be Founder's Office, Real Estate, Hospitality, Co-living, Asset Management, Facilities, REVA University. The objective is to embed with that team, and ship something that works before you move to the next one. Some deployments last days, some last a quarter. Every one ends the same way: a working system, and a team that can run it without you.

How deployments work

• Founder's Office is one discipline you may be deployed into — fast, high-ambiguity POC work directly for the founders, days-to-decision, kill-or-scale
• Other deployments run longer and go deeper — embedding inside a business unit (e.g. Leasing, Co-living Ops, Hospitality, REVA University, Facilities) to take something from idea to a system that's actually adopted and still running after you leave
• You're assigned based on where the Group's priorities are, not a fixed team — expect to move across disciplines multiple times a year
• Every deployment ends with a handoff: either the receiving team can operate and extend what you built, or you've written a clear, evidence-backed brief on why it shouldn't be scaled

What you'll do

• Sit with whichever business unit or founder you're deployed to, understand the real problem, and define a clear technical plan often before anyone else has scoped it.
• Build and ship production code, prototypes, and everything between, using whatever the problem demands: data pipelines, LLM orchestration across providers, backend services, internal tools, deployment on AWS.
• Decide, every time, whether AI/LLMs are actually the right tool for the problem and say so plainly when they aren't.
• Present your work directly to the stakeholders (founders, business unit heads, functional teams) who asked for it. You own the narrative, not just the code
• Train the team you're embedded with so what you built keeps running without you
• Contribute reusable platforms, frameworks, and standards back to the AI Initiatives COE so the next deployment moves faster than the last

What makes you stand out

• You've used AI coding tools (Claude Code, Cursor, or similar) as your daily driver for a meaningful stretch and not something you tried once
• You orchestrate agents, not just prompt them. You build with skills, MCPs, and multi-step automation, and it's muscle memory
• You have a public or demonstrable artifact, a shipped project, a hackathon win, a repo, something you can point to and say I built this, end to end
• You've shipped something into a real workflow, even an informal one, that's still running without you
• You're comfortable with zero spec and being told just figure it out
• You're a fast generalist. You'd rather learn lease accounting, hospitality operations, or campus administration on the fly than wait for a briefing document
• Founder mindset: you've taken something from a blank page to working, on your own hands, at least once
• Comfortable being the only technical person in a room of non-technical stakeholders, and can explain your work in their language

Who should apply

This isn't limited to computer science grads or people already carrying an AI Engineer title. We're open to anyone, from any educational or professional background, at any stage: interns, fresh graduates, or experienced professionals from any discipline who've been wanting to make the move into AI and can show they've already started. What matters is what you've built, not what your degree or job title says.

How to apply:

The Build TrackWe're skipping the resume screen and the standard interview funnel for this role. Instead, you pick a problem statement below (or propose your own under the Open Track), build a working solution against it, and submit it. Strong submissions get called in directly for a live panel. No aptitude test, no group discussion in between.

What to submit, to [Confidential Information]:

1. A public GitHub repository with your code. Include a clear README covering what it does, how to run it, and how it's set up.
2. A 5-minute pitch video (unlisted link is fine): Lead with the problem, then your solution, then a live demo of it actually working. Spend the most time on the AI-native part: if your project touches agents, orchestration, or LLM reasoning, that's what we want to see in action. This is optional but recommended.
3. A short written note (half a page is enough) covering: what tradeoffs you made and why, where this would break at scale or on real data, and where you did or didn't use AI and why.

Send the repo link, video link, and note in a single email to [HIDDEN TEXT], with the track name in the subject line. Feel free to also share links to anything else you've built that you'd like us to see (other repos, side projects, live products, anything that shows how you think and build)

What we're evaluating:

• AI judgment: Did AI/LLMs genuinely add value here, or were they forced in because the brief mentions AI
• Speed-to-signal: How much real progress did you make in the time given, versus over-engineering a narrow slice
• Failure awareness: Do you know where this breaks, and have you said so honestly
• Communication: Can you explain this to a non-technical founder or business head in plain language
• Scope instinct: Did you pick something narrow enough to actually finish

What happens next:

Shortlisted submissions move to the one-hour, hands-on technical call described below. From there, strong candidates go straight to an offer conversation — there's no additional round.

Problem statement

Pick one, or propose your own under the Open Track. Every track uses the same submission format above (GitHub repo + README, 5-minute pitch video, short note).

Open Track

Propose and build your own idea for a real problem anywhere in the Group: real estate, hospitality, co-living, facilities, or asset management. A working prototype, plus the same note format as above.

What the technical call looks like

Shortlisted from a Build Track submission, or applying directly: the first technical call is one hour, hands-on, and runs entirely off your screen. We'll walk through what you built, then hand you a new, live, underspecified problem and watch how you scope it and start, not whether you finish it.

What you'll get

• Broad exposure: you'll work across nearly every part of a diversified Group spanning real estate, hospitality, co-living, facilities, and education within your first year
• Real ownership from week one, wherever you're deployed: your work is load-bearing, not a side project
• Freedom to use whatever models, agents, frameworks, and tools work best for the problem in front of you
• Direct access to founders and business unit leaders: your work is judged on impact, not process
• A growing track record of shipped, adopted systems across the group, not just POCs
• High-variance work by design: some deployments are two-day sprints, some become the system a whole business unit runs on

DivyaSree Group is an equal opportunity employer and does not discriminate on the basis of gender, religion, sexual orientation, colour, nationality, age, or any other protected characteristic.