SDE-2 AI Pod

About Kissht :

Kissht is a digital lending company. We listed on the Indian stock exchanges on May 8, 2026. Our consumer product is Ring a lending and transactions platform used by millions of customers. We are building one of Indias most modern lending stacks.

Why we are hiring an AI Pod :

AI is changing how lending works. The opportunities for Kissht sit across five capability areas :

- Document intelligence across Indian languages

- Voice AI in Indian languages

- Agentic workflows

- Internal AI productivity

- Measurement and evaluation

We are setting up a dedicated AI Pod to drive work across these capability areas and to set the template for how AI work gets done across Kissht.

How we work :

- Speed positive results is our P0. We prefer shipping quickly with disciplined measurement over long planning cycles.

- Ownership over consensus. We expect people to make decisions, defend them, and adjust based on signal not to seek alignment for its own sake.

- Joint outcomes. The pod is evaluated as a team, on business outcomes, not as individuals on local KPIs.

- AI-first. Every pod member uses modern AI assistant and coding tools daily. We have enterprise LLM agreements that allow PII-safe usage. We build in-house where we get IP value, and we buy where we dont.

- Embedded, not siloed. The pod ships into production through business teams, not on a separate roadmap. We measure ourselves by what reaches customers, not by what we prototype.

Data and security discipline :

Kissht is a regulated lender and a listed company. We have active DLP monitoring on GenAI tools. We are explicit about which tools are authorized for PII work and which are not. Pod members must internalise this discipline from day one.

The Role :

SDE-2 AI Pod (2 hires). The pod needs two strong generalist engineers who can move across the entire AI build surface quick prototypes, production integrations, internal eval tooling, vendor-facing glue code. We are not splitting these into prototyping and integration specialists. The work is wide. The engineers we hire should be too.

Responsibilities building and delivery :

- AI features end-to-end across Voice, LAP, Customer Service, and Onboarding from prototype to production, depending on what each initiative needs at the moment.

- Internal tools that make the pod faster : eval dashboards, prompt playgrounds, data explorers, integration scaffolding.

- Production integrations between external AI vendors and Kissht systems APIs, webhooks, data pipelines, observability.

- Compliance with Kisshts data and security standards. You design for PII safety from the first commit.

- Pair work with the AI Product Lead and Tech Lead to translate specs into shipped artefacts.

What success looks like first 90 days :

- You have shipped working code on at least two of the four initiatives.

- At least one thing you built is in daily use either by the pod (an internal tool) or by a business team (a production feature).

- You have a working relationship with at least three Kissht engineering teams whose systems the pod touches.

What success looks like first 180 days :

- You are the named owner of at least one AI feature in production.

- You have moved fluidly between greenfield prototype work and production integration work, depending on initiative need not stayed inside one lane.

- You have started embedding more directly with one business team owning AI outcomes inside their roadmap, not just inside the pod's.

Must-haves :

- 3 to 5 years of engineering experience.

- Strong Python.

- Comfortable with backend services, REST, async patterns, and webhooks.

- Genuine versatility. You have shipped quick prototypes and you have run production integrations. You do not specialise in only one of those.

- High comfort with LLM APIs. You have built non-trivial things on production-grade LLMs.

- Bias for action. You decide and move with incomplete information, ship a working thing in 23 days when needed, and stand behind it in production.

- Proof of building. GitHub repos with real code are required.

- Side projects, hackathon work, or open-source contributions count.

- A polished LinkedIn alone is not enough.

Nice-to-haves :

- Frontend skills (React or similar) useful for quick demos and internal tools.

- Experience integrating with telephony providers or contact-centre platforms.

- AWS production experience, familiarity with Streamlit/Gradio/Chainlit, and feature flag tooling.

- Snowflake experience or comfort working against a cloud data warehouse.