Forward Deployed Engineer

Rhai is an AI consulting group. We believe the future is already here, it's just unevenly distributed (Gibson) — and we want to help deploy it to Indian companies to bring the future here.

We help companies deploy AI in three ways:

• Teaching AI workshops to align teams and orient them
• Building the tech they need
• Providing other consultancy and services — GTM, rebranding, ops analysis using AI to strengthen the process

We're not trying to reduce headcount — that's a narrow-minded way to think about the new paradigm AI makes possible.

Think about a sales executive. Her job is about an objective: to sell more. But her day is about tasks: answering emails, updating the CRM, chasing follow-ups, formatting the same sheet three different ways. We build the tools that make jobs about outcomes again.

We do this with AI in three places: to find patterns in a company's data, to help us build custom tools fast, and inside the product itself — so the dashboards we ship don't just make the company legible to people, they make it legible to AI. Dashboards going from reports to operators.

None of this works without trust.

Deployment happens at the speed of trust.

For this role that's true twice over — you'll be inside a company's real systems and data.

The role

You'll embed in a client company, learn how its systems actually work, and build the tools it needs — on the company's own stack, with the Rhai agent as your pair. You are an engineer, but not the kind who hand-writes every line. The agent writes most of the code. Your job is to bring the systems thinking, the abstraction sense, and the taste that decides what to build and what "good" looks like — and the judgment to touch production systems safely.

We're not looking for ten years in one framework. We're looking for someone who can stare at a tangle of spreadsheets and half-broken processes and see the clean data model underneath — who thinks in interfaces, invariants, and layers, knows when to abstract and when not to, and can move from a messy business problem to a working tool fast.

What you'll actually do

• Embed in a client — a week or more, onsite. Learn their systems, their data, their stack, and the way work actually flows versus how the org chart says it does.
• Model the domain — turn the mess of sheets, processes, and edge cases into a clean data model and a sane architecture. This is the part that decides whether everything downstream is simple or a nightmare.
• Build the tools — dashboards, integrations, automations — with the Rhai agent, on the client's own Google or Microsoft tenant, their APIs, their data. You direct the agent, review and correct its output, and own the structure. Nothing to migrate afterwards; they keep everything.
• Integrate safely — auth, data integrity, failure modes, reversibility. You're touching things that matter, so you think about blast radius before you ship.
• Make it stick — sit with the people who'll actually use it, fix what's awkward, and hand over something that gets adopted rather than admired once and forgotten.
• Report back to the founder as you go — what the systems really look like, what you're building, what's fighting you, and what you don't yet understand.

You'll probably be great at this if you

• Think in systems and abstractions — you can model a messy domain cleanly, and explain why you modelled it that way
• Have shipped real software end to end — any stack, scrappy is fine; you've felt what breaks in production
• Are AI-native about building — you already build with AI agents, and you have the taste to know when the output is right and when it's quietly wrong
• Know when not to abstract — you've been burned by the wrong abstraction and learned from it
• Care about other people's data and systems the way you'd want someone to care about yours
• Can explain a technical idea to a non-technical person without condescending
• Are comfortable being embedded in a room of strangers and earning their trust

You're probably not the right fit if you

• Want a heads-down IC role writing every line yourself, away from users — this is the opposite
• Think AI-assisted building is beneath "real" engineering
• Over-engineer by default, or can't operate without a precise spec
• Get careless around production data, or impatient with people slow to trust new tools

Logistics

• Based in Bangalore; must be willing to travel and stay in a client city for the duration of an engagement
• Compensation: ₹6 LPA
• Full-time or contract
• Start: rolling

To apply

The first round is a conversation with Rhai — the AI agent you'd actually build alongside. About fifteen minutes, by voice or text, whenever suits you. If it goes well, the second round is with Rhea.