Lead Engineer ( Agentic AI)

Yubi

Yubi

Software Engineering, Data Science

Bengaluru, Karnataka, India

Posted on May 26, 2026

About Us

Yubi stands for ubiquitous. But Yubi will also stand for transparency, collaboration, and the power of possibility.
From being a disruptor in India’s debt market to marching towards global corporate markets from one product to one holistic product suite with seven products
Yubi is the place to unleash potential. Freedom, not fear. Avenues, not roadblocks. Opportunity, not obstacles.

Job Description

ABOUT YUBI

Yubi (formerly CredAvenue) is redefining global debt markets by freeing the flow of finance between borrowers, lenders, and investors — the world's possibility platform for the discovery, investment, fulfilment, and collection of any debt solution. In March 2022, we became India's fastest fintech unicorn with a $137M Series B. Our platforms — Yubi Credit Marketplace, Yubi Invest, Financial Services Platform, Spocto, and Corpository — serve 17,000+ enterprises and 6,200+ investors, facilitating over ₹1,40,000 crore in debt volumes. Backed by Insight Partners, Sequoia Capital, Dragoneer, B Capital, LightSpeed, and Lightrock.

ROLE OVERVIEW

As a Lead Engineer (Agentic AI) at Yubi, you will architect and deliver production-grade intelligent systems that redefine how debt capital flows between borrowers, lenders, and investors. You will lead a pod of engineers, own the technical roadmap for key AI agent capabilities, and work hand-in-hand with Product and Data Science to bring ambitious ideas to life at scale. This role demands deep full-stack expertise across Java, a strong grasp of LLM orchestration, and the leadership instinct to elevate the entire team around you.

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KEY RESPONSIBILITIES

Technical Leadership & Architecture

  • Own the end-to-end architecture of multi-agent systems — from LLM reasoning layers to React dashboards, Python, and Java microservices.

  • Define technical standards, design patterns, and coding practices for the AI platform engineering pod.

  • Lead design reviews, ensuring systems are scalable, secure, observable, and maintainable.

  • Evaluate emerging AI frameworks and tools; make adoption decisions that balance innovation with production reliability.

  • Drive migration of rule-based financial workflows to LLM-native agentic pipelines.

Agentic AI Engineering

  • Architect multi-agent orchestration using LangGraph, AutoGen, or CrewAI — supporting tool use, memory, planning, and reflection loops.

  • Design stateful agent memory systems combining short-term context windows with long-term vector stores for financial workflows.

  • Build reliable prompt engineering strategies with versioning, A/B testing, and rollback capabilities.

  • Implement compliance-aware guardrails: PII masking, hallucination detection, regulatory disclosure injection.

  • Oversee RAG pipeline design — chunking strategies, embedding model selection, retrieval optimisation, and re-ranking.

People & Delivery

  • Lead, mentor, and grow a team of 3–6 engineers; conduct regular 1:1s, code reviews, and career development conversations.

  • Drive sprint planning, estimation, and delivery commitments in close partnership with the Product Manager.

  • Participate in senior technical hiring — define interview criteria and assess engineering bar.

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Requirements

REQUIRED SKILLS & QUALIFICATIONS

Technical

  • 5–8 years of full-stack engineering experience, with at least 1-2 years in AI/LLM product development.

  • Very strong problem solving capability . Doing HLD and LLD for a given problem

  • Good understanding of data structures , algorithms

  • Good Understanding of CI/CD pipelines and test driven development

  • Proven experience building production LLM-powered products — prompt engineering, agent orchestration, output validation.

  • Hands-on with LangChain, LangGraph, AutoGen, or equivalent agent frameworks.

  • Strong understanding of RAG, vector search, and embedding-based retrieval.

  • Experience with Kafka or similar message brokers; familiarity with event-driven architecture patterns.

  • Kubernetes, Docker, and cloud-native AWS experience at production scale.

Leadership & Soft Skills

  • Proven track record of leading engineering teams and delivering complex projects end-to-end.

  • Excellent communication — able to translate AI system behaviour to product, business, and compliance stakeholders.

  • Strong systems thinker with the ability to decompose complex financial workflows into reliable automated steps.

  • B.E. / B.Tech / M.Tech in Computer Science or equivalent; advanced degree a plus.


Benefits



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