AI Agent Engineer

We are building an AI-native analytics function. Instead of analysts hand-writing every query and report, we want people who design AI agents that explore lending data, find insights and recommend actions on their own. You will connect LLMs to our data warehouse and internal tools. You will write the prompts, guardrails and evaluation loops that make the agents reliable.

You don't need to be a lending expert. You need working knowledge of how lending and collections work, and deep skill in building and prompting agents. We'll teach the domain. We can't teach you to build good agents from scratch.

What You'll DoDesign, build and ship LLM-based agents that query Snowflake, analyse portfolios and produce insights, such as roll rates, bucket movement, cohort performance and campaign effectiveness.Write and maintain production-grade system prompts, tool definitions and knowledge bases, so agents apply business rules correctly (for example, block-list filters, DPD definitions and loan-prefix logic).Build multi-step and multi-agent workflows: planning, SQL generation, validation, summarization and anomaly flagging.Create evaluation harnesses (golden questions, SQL-correctness checks, hallucination tests) to measure and improve agent accuracy.Automate recurring analyses: daily and weekly performance reports, pre-due and delinquent campaign tracking, field-visit and agency productivity analysis.Turn agent output into dashboards, artifacts or alerts that collections, risk and product teams use.Work with business stakeholders to turn vague questions ("why did the 30+ bucket worsen?") into agent-solvable tasks.Document agent behaviour, failure modes and costs, and keep token and compute usage under control.

Must-Have SkillsAgent building: hands-on experience with tool-use / function calling and agent loops, using the Claude API/SDK, MCP, LangChain/LangGraph or similar.Advanced prompting: structured prompts, few-shot examples, chain-of-thought, output schemas, prompt versioning and debugging hallucinations.SQL (strong): Snowflake or a similar warehouse. You must be able to check whether an agent's SQL is right, not just trust it.Python: pandas, API integrations, scripting, basic orchestration (Airflow or cron).Insight generation: you can go from raw data to "what changed, why, and what should we do".Evaluation mindset: you test agents systematically and don't ship on vibes.Lending basics: understanding of DPD, buckets, NPAs, roll rates, collections efficiency, EMI cycles and credit-risk fundamentals.

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.

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 InvestmentAuthority, Vertex Growth Fund, and the Government of Singapore, Kissht is now scaling as a publiccompany and is looking for driven, entrepreneurial talent to join nits next phase of growth.