Tech Lead

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.
• Strong 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, a Great Place to Work® certified organization, is a consumer-first credit app that is transforming the landscape of consumer credit. As one of the fastest-growing and most respected FinTech companies, Kissht is a pioneer in data and machine-based lending. With over 15 million customers, including 40% from tier 2 cities and beyond, we offer both short and long-term loans for personal consumption, business needs, and recurring expenses.

Founded by Ranvir and Krishnan, alumni of IIT and IIM, and backed by renowned investors like Endiya Partners, the Brunei Investment Authority, and the Singapore Government, Kissht is synonymous with excellence in the industry. Join us and be a part of a dynamic, innovative company that is changing the future of financial technology.