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.
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 Kissht's 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
- 3–5 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 2–3 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.
