Principal Engineer

Mintifi Finserve Pvt Ltd

Role - VP - Principal Engineer Platform Engineering & Applied AI

Function - Technology — Engineering

Level - Senior leadership (VP band) — technical / individual contributor

Reports to - CTO

Location - Mumbai

Type - Full time

About Mintifi

Mintifi is one of India's leading supply chain financing platforms, financing the dealers, distributors and vendors of large corporate anchors. Our lending runs on a stack of core systems — including our Loan Origination System (LOS) and Loan Management System (LMS) — and a growing set of distributed services built to scale.

The Role

We're looking for a Principal Engineer to be one of Mintifi's deepest technical hands — a builder who sets the engineering bar through code and design, not through a management chain. You'll own the architecture and the hard technical problems across our Java-based lending platform: designing core backend services, building AI-enabled product features, driving performance and scale, and keeping a strong security posture across a regulated lending business. This is a hands-on, individual-contributor role. You'll influence architecture across teams and mentor engineers, but your primary impact is technical — the systems you design and the standards you set.

What You'll Own

Architecture & hands-on engineering – Design and build core backend services in Java across the lending platform (LOS, LMS and distributed services), owning end-to-end technical design of complex features. – Set engineering standards — clean, well-tested, maintainable code — and raise the bar through design reviews and hands-on work, contributing across the stack (including a working level of React) where it helps.

Building AI-enabled products – Bring practical, applied AI into the product — integrating modern AI services and models (e.g. LLMs and AI APIs) into lending workflows to ship genuinely AI-enabled features. This is applied AI. – Put the right engineering around AI features — evaluation, guardrails, cost and latency control, monitoring, and safe handling of sensitive data — and use AI tooling to lift the wider team's productivity.

Performance, scale & reliability – Own performance engineering: profile, tune and scale services for growing transaction volumes at low latency. – Design for reliability across distributed, microservices-based systems — fault tolerance, observability and capacity planning.

Security & cybersecurity – Keep a security-first posture: secure coding, threat modelling, data protection, secrets management and sound authentication / authorisation, as befits a regulated lender. – Bring strong cybersecurity awareness to design and reviews — including the security implications of AI features — and partner with security and QA on VAPT remediation and compliance-aligned engineering.

Cloud & data – Architect and run services on AWS, with Kubernetes, containers and CI/CD; shape data solutions on relational databases (primarily PostgreSQL) and data lake / analytical stores, with strong data modelling and query performance.

Domain & influence – Bring deep lending-domain understanding to technical decisions across origination, underwriting, disbursement, servicing and collections.

What You Bring – Expert-level Java and deep software-engineering craft, with a long track record of building production systems at scale. – Hands-on experience building AI-enabled products — integrating AI / LLM services into real applications (applied AI, not model training). – Good cybersecurity knowledge and security-engineering practice — secure coding and data protection in a regulated environment. – Strong performance engineering and systems design across microservices and distributed architectures. – Hands-on AWS experience and cloud-native delivery, plus real lending / fintech domain expertise. – A genuine builder's disposition — this is a technical IC role, not a management track.

Good to have – Depth with Kubernetes and with data solutions on PostgreSQL and data lake / analytical stores; working React and full-stack comfort. – Familiarity with AI frameworks, RAG, agents or vector stores, and with AI-assisted developer tooling. – Event-driven / streaming (e.g. Kafka), caching, and experience in RBI /