Software Engineer

Krazy Bee Services; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

KreditBee in Bengaluru is hiring for two tracks: Software/Platform Engineer and ML/Applied-AI Engineer. You will own end-to-end development, ship reliable distributed systems, and contribute to a regulated fintech platform.

You will design, build, test, and operate model-serving, data pipelines, and evaluation frameworks, with an emphasis on reliability, observability, and safe data handling in a highly regulated environment.

Qualifications

• Experience shipping production-grade backend systems.
• Strong CS fundamentals and system design.
• Familiarity with distributed systems and APIs.

Responsibilities

• Own end-to-end design, build, test, ship, monitor, and iterate features.
• Write clean, well-tested code and clear design docs.
• Prioritize reliability, latency, cost, and observability from the start.
• Contribute to data privacy, access controls, and auditability in a regulated fintech setting.
• Collaborate with product, data, risk, and platform teams to solve ambiguous problems.

Skills

Distributed systems

Go/Java/Python

APIs

Databases

Cloud infrastructure

Reliability mindset

Job description

About the role

KreditBee is building an AI platform that lets every team ship Gen AI features safely and at scale retrieval, agent workflows, model serving, evaluation, and guardrails. As a regulated consumer-lending business, we hold reliability, consistency, and auditability above all else; this platform is the foundation the org depends on to become AI-native.

It's a single role family with two tracks and a shared engineering bar. You apply to Mandate Skills

the family; we place you on the track that fits your depth and the level that fits your experience, and you can move between tracks as the platform grows.

• Software / Platform track - reliable, well-designed distributed systems: the serving, orchestration, data, and tooling layers that make AI usable in production.
• ML / Applied-AI track - making models work: retrieval quality, prompting and agent design, evaluation, and fine-tuning, with production-grade code.
• Owns features end-to-end with light oversight; reliable, independent execution.

What you'll do - shared across both tracks

• Own work end-to-end - design, build, test, ship, monitor, iterate - with production ownership and on-call.
• Write clean, well-tested, maintainable code and clear design docs; apply SOLID and sound API design.
• Treat reliability, latency, cost, and observability as first-class requirements, not afterthoughts.
• Build for a regulated environment: data privacy, access controls, auditability, safe handling of customer data.
• Collaborate across product, data, risk, and platform to turn ambiguous problems into measurable outcomes.

Track A - Software / Platform Engineer

• Build and operate model-serving, gateway, and orchestration infra (routing, caching, rate-limiting, fallbacks) for LLM/ML workloads.
• Design data and RAG pipelines - ingestion, chunking, embedding jobs, vector/index stores - that stay fresh and consistent.
• Build guardrails, evaluation harnesses, prompt/version management, and observability (tracing, metrics, cost attribution); harden for scale and failure.
• We look for: production backend/distributed systems in a strong language (Go, Java, Python); solid concurrency, APIs, databases, queues, and cloud infra; a reliability mindset. Deep ML theory not required.

Track B - ML / Applied-AI Engineer

• Improve retrieval and RAG quality - chunking, embeddings, re-ranking, grounding - measured against real metrics.
• Build agent and prompt workflows; systematically evaluate models, prompts, and pipelines with offline and online evals.
• Fine-tune, adapt, or distill models where it clearly beats prompting; partner with the platform track to productionize to the same reliability bar.
• We look for: production-quality Python and service ownership (not just notebooks); hands-on LLMs, embeddings/retrieval, and evaluation, plus one of fine-tuning, RAG, or agent frameworks; rigor with data and experiments.

Common bar & nice-to-haves

• Strong CS fundamentals (data structures, algorithms, system design) and a track record of shipping in production.
• Clear communication and a bias for reliability and correctness - especially important in fintech.
• Nice to have: fintech / lending / payments or other regulated domains; LLMOps / MLOps tooling, vector DBs, eval frameworks; open-source or AI/ML side projects.