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 Principal Engineer (Agentic AI), you will be the technical visionary behind Yubi's AI platform — setting the long-term architecture direction, establishing engineering excellence standards, and driving the adoption of agentic AI across our entire product suite. This is a hands-on leadership role: you will write critical code, mentor senior engineers, define platform-level abstractions, and partner directly with the CTO and VP Engineering to shape Yubi's AI strategy. Your work will determine how intelligent agents transform the future of debt markets at scale.

KEY RESPONSIBILITIES

Platform Architecture & Strategy

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  Define the multi-year technical vision and architecture for Yubi's Agentic AI platform — spanning agent runtimes, LLM integration layers, data pipelines, and developer tooling.

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  Establish platform-level abstractions and internal frameworks that accelerate agent development across multiple product teams.

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  Lead architectural reviews across engineering, ensuring AI systems are scalable, explainable, compliant, and cost-efficient at millions-of-transactions scale.

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  Identify build vs. buy decisions for AI infrastructure; evaluate and adopt emerging frameworks, foundation models, and tooling ahead of the curve.

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  Define and own AI engineering standards: prompt versioning, evaluation frameworks, guardrail policies, and responsible AI practices.

Hands-On Agentic AI Engineering

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  Design and build the most complex, high-stakes agent systems — multi-agent orchestration, long-horizon planning, reflection loops, and tool-augmented reasoning.

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  Architect LLM fine-tuning and alignment workflows for domain-specific financial tasks where general models fall short.

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  Own the agent evaluation and reliability framework — golden datasets, LLM-as-judge pipelines, regression suites, and production monitoring.

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  Drive RAG platform evolution — advanced retrieval, hybrid search, knowledge graph integration, and context compression.

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  Build developer experience tooling — internal SDKs, agent testing harnesses, and observability dashboards used by all AI teams.

Organizational Impact

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  Mentor and coach Lead, and Senior engineers across AI teams; act as a technical multiplier across the organization.

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  Define the engineering hiring bar for AI roles; lead final-round technical interviews and calibration sessions.

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  Represent Yubi's engineering at external conferences, tech talks, and open-source communities to attract top talent.

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  Collaborate with CTO, VP Engineering, and business leadership to translate AI opportunities into executable technical roadmaps.

Requirements

REQUIRED SKILLS & QUALIFICATIONS

Technical

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  8–12 years of full-stack engineering experience; 2+ years in production LLM / AI agent system development.

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  Very strong problem solving capability and doing HLD and LLD for a given problem

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  Good understanding of data structures , algorithms

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  Good Understanding of CI/CD pipelines and test driven development

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  Deep expertise in Spring Boot, Spring Cloud, and Hibernate for enterprise-grade, high-throughput microservices.

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  Deep Understanding of Observability frameworks , Async processing frameworks

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  Strong React/TypeScript and Node.js skills; experience with large-scale frontend architectures.

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  Proven track record of architecting and shipping production LLM-powered systems handling significant load.

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  Expert-level knowledge of agent orchestration (LangGraph, AutoGen, custom runtimes), RAG pipelines, and LLM evaluation.

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  Deep experience with Kafka, event-driven architectures, and large-scale data pipelines.

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  Kubernetes, AWS (EKS, MSK, RDS, SageMaker), and infrastructure-as-code expertise.

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  Strong understanding of responsible AI — explainability, bias mitigation, hallucination control, and regulatory compliance.

Leadership & Soft Skills

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  Demonstrated technical leadership across multiple teams and products simultaneously.

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  Exceptional written and verbal communication — able to influence executives, engineers, and external partners.

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  Track record of building and growing high-performing engineering teams.

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  Strategic thinker who can operate at both the architecture white-board and the code editor.

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  B.E. / B.Tech in Computer Science or related field; M.Tech / PhD advantageous.