Navdeep Virdi

Senior Software Engineer

India7 yrs 4 mos experience

AI EnabledHighly Stable

Key Highlights

Led teams to build scalable SaaS products.
Achieved significant growth in transaction volumes.
Expert in Node.js and React.js for high-performance applications.

Stackforce AI infers this person is a Senior Software Engineer with expertise in SaaS and Fintech, focusing on scalable web applications.

Contact

navdeep.virdi@seekout.com LinkedIn

Skills

Core Skills

Node.jsReact.jsFull Stack DevelopmentApi Development

Other Skills

Agile PrinciplesAlgorithmsApplication Programming Interfaces (API)Artificial Intelligence (AI)Backend DevelopmentBackend ServicesCC++Computer ScienceCustomer OnboardingDCTData ScienceData StructuresDesign PatternsExpress.js

About

Senior Software Engineer with 7 years of strong experience building and scaling products across SaaS and fintech. Currently a founding engineer at SeekOut India, leading a team and building core platforms from the ground up. Specialized in Node.js, React.js, and Next.js with a focus on scalable backend systems, distributed microservices, and high-performance web applications. Previously helped scale the Instore business at ZestMoney and worked on large-scale banking systems at HSBC. I enjoy owning problems end to end, mentoring teams, and building products that create real business impact.

Experience

Seekout

Senior Software Engineer

Jun 2024 – Dec 2025 · 1 yr 6 mos · Bengaluru, Karnataka, India · Hybrid

Led a team of 4 developers while collaborating with Product and Business teams
to define requirements and ship core products from the ground up.
Redesigned and scaled the messaging platform used by more than 10,000
recruiters for outreach to millions of candidates, improving system performance by
40%, increasing reliability, and significantly boosting email deliverability.
Drove backend (Node.js), frontend (Next.js/React.js), and UX improvements
across multiple microservices, reducing system errors by 30%, improving load
times, and streamlining key user workflows.

Node.jsNext.jsReact.jsMicroservicesBackend DevelopmentFrontend Development

Zestmoney

SDE2

Sep 2021 – May 2024 · 2 yrs 8 mos · Bengaluru · Hybrid

Led a team of 3 developers and expanded the Instore business by building robust web applications using Node.js and React.js, contributing to 80% percent growth in transaction volume.
Implemented a QR based customer onboarding system, replacing the older flow
and resolving major operational bottlenecks.
Engineered scalable, low latency backend services for managing orders,
supporting a user base of more than 1M+ customers and handling over 100 rps in
production.

Node.jsReact.jsWeb ApplicationsCustomer OnboardingBackend Services

Hsbc

Software Engineer

Jul 2018 – Sep 2021 · 3 yrs 2 mos · Pune, India

Full Stack Development:
Extensive experience in developing maintaining various full-stack
applications and RESTful APIs across multiple platforms with MERN stack.
Led the development of a customer-facing dashboard which integrates
static and dynamic data related to various APIs. Built reusable
components with React.js, Node.js and JavaScript ES6.
Hands on experience in creating frontend components using React.js,
HTML5, CSS3, and JavaScript.
API Development:
Handling Dev to Prod work of API development under Agile principles by
collaborating with Consumer, Backend, BA and Data Modeler.
Developing Mule Flow using Anypoint Studio, Unit and Functional Testing
of Mule Flow using Soap UI and Munit. Deploying Developed Mule App
to Cloud Foundry for Quality Assurance and Certification.
Monitoring application and Tracking performance of the API using App
Dynamics and Splunk. Troubleshooting & Bugs fixing for the APIs in
production.

Full Stack DevelopmentRESTful APIsMERN StackAPI DevelopmentAgile Principles

Changwon national

Research And Development Intern

May 2016 – Jul 2016 · 2 mos · South Korea

Illumination Invariant Face Recognition Using High order Local Discrete Patterns: (under the guidance of Prof. Seol Kwon, Department of ECE )
DCT on input image was applied in logarithmic domain and low frequency components responsible for illumination variation were discarded. LDP features were extracted from illumination invariant image.
Average accuracy of 94% was obtained using histogram comparison method.

Face RecognitionImage ProcessingDCTHistogram Comparison