Role Overview

This is the pod's most critical hire. You set the technical direction for every piece of AI work at Kissht - architecture, integration patterns, evaluation infrastructure, and the call between vendor APIs and self-hosted models. When someone asks how do we do AI engineering here, the answer is yours to give.

Responsibilities - building and delivery

- AI platform architecture: LLM integrations, RAG pipelines, agent frameworks, context engineering, evaluation harnesses, observability.
- Build-vs-buy technical calls, in partnership with other stakeholders.
- Engineering standards for AI work across Kissht: how we prompt, evaluate, monitor, and iterate.
- Technical vendor evaluations: API testing, latency and accuracy benchmarks, integration complexity, red-flag surfacing.
- Mentoring the two SDE-2 Builders and the Data Engineer. You set the quality bar.

What success looks like - first 90 days

- The pod's AI architecture is documented, reviewed, and being followed in active builds.
- The evaluation harness used by the pod is operational and used in at least one live vendor eval.
- The two SDEs are productive and unblocked technically.

What success looks like - first 180 days

- One in-house AI orchestration layer is in production, owned by the pod, and integrated with at least two Kissht systems.
- The build-vs-buy decision flow has been used on at least three vendor decisions, with documented rationale.
- AI engineering standards are visible to non-pod engineers and at least one team outside the pod has adopted them.

Must-haves

- 7–8 years of backend engineering experience. At least 2 years building LLM-based products in production.
- Deep familiarity with at least one production-grade LLM ecosystem and its orchestration patterns.
- Hands-on experience with RAG architecture, vector databases, evaluation pipelines, and context engineering.
- Solid systems thinking: prompt → data pipeline → model → integration → monitoring, end to end.




- Strong Python. Comfortable reading and writing production-grade backend code.
- Bias for action. You decide and move with incomplete information, rather than waiting for full certainty.
- 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

- Experience in financial services integrations, real-time voice AI, or document intelligence.
- Experience leading a small team of 3–4 engineers.
- Cloud architecture experience on AWS.
- Snowflake experience or comfort designing on top of a cloud data warehouse.

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.

About Kissht:

Kissht is India's leading listed fintech companies, offering fast, personalized digital credit through its mobile app to customers across the country. Founded in 2016 by IIT/IIM alumni Ranvir Singh and Krishnan Vishwanathan, both former McKinsey consultants, Kissht was listed on the NSE and BSE in May 2026 with a market capitalization of approximately ₹5,200 crore.

Kissht serves 74.6 million registered users and 12.25 million customers, with assets under management (AUM) of approximately ₹8,000 crore. Its growth is powered by proprietary AI/ML-based underwriting, a robust in-house technology stack, and a pan-India collections network. Headquartered in Mumbai and backed by marquee investors, including the Brunei Investment Authority, Vertex Growth Fund, and the Government of Singapore, Kissht is now scaling as a public company and is looking for driven, entrepreneurial talent to join its next phase of growth

📌 Lead AI Engineer (Mumbai)
🏢 Kissht
📍 Mumbai