Technical Support Engineer · Customer-facing engineering

Mohit Kumar.

I investigate customer-impacting problems across APIs, integrations, databases, and full-stack applications. I turn ambiguous reports into reproducible findings, clear next steps, and targeted fixes that make SaaS products more reliable.

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About

How I work

I investigate complex customer-reported issues across a SaaS platform, tracing failures through frontend experiences, backend services, APIs, databases, and third-party integrations. My work combines careful reproduction, root-cause analysis, and clear technical writing.

I use production logs and database analysis to understand impact, then decide whether a focused fix is appropriate or an escalation needs deeper engineering ownership. I communicate the evidence and trade-offs clearly to customers, product partners, and engineers.

Skills

Stack & domains

Languages

Java
JavaScript
TypeScript
SQL

Frontend

React
JSX
HTML
CSS

APIs & integrations

REST APIs / webhooks
OAuth
Third-party integrations
Request and response debugging

Systems

PostgreSQL
Docker
AWS
Git / Linux

Support engineering

Incident triage
Bug reproduction
Root-cause analysis
Technical documentation

Experience

Professional background

Investigate complex customer-reported issues across a SaaS platform, translating symptoms into reproducible technical findings.
Trace failures across frontend experiences, backend services, APIs, and third-party integrations to identify root cause.
Use production logs and database analysis to assess impact, prioritize investigations, and support evidence-based decisions.
Implement focused fixes and tests when appropriate, then verify behavior after release.
Coordinate with product and engineering on escalations, clearly documenting reproduction steps, impact, trade-offs, and recommended next steps.

My approach

How I solve problems

01Report

02Reproduce

03Investigate

04Measure impact

05Fix or escalate

06Verify

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NBA All-Star Predictor

Problem: All-Star selection is high-stakes and opaque; stakeholders need a data-driven view of who is likely to make the cut. Approach: Built an ML classifier on historical player stats with feature engineering and model comparison in Scikit-learn, shipped as an interactive Streamlit app for exploration. Impact: Demonstrates data analysis, model evaluation, and a clear user-facing surface for non-technical users.

Python

Scikit-learn

Streamlit

Pandas

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Ride1Up e-commerce experience

Problem: Real storefronts require coherent routing, state, and presentation across many product flows. Approach: Cloned a production-style bike e-commerce UX with React Router and Redux for predictable global state, and CSS Modules for maintainable styling boundaries. Impact: Shows how I structure larger frontends with predictable state and maintainable feature boundaries.

React

Redux

React Router

CSS Modules

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Dictionary web app

Problem: Users need fast definitions, phonetics, and related word context from a single search. Approach: Consumed a dictionary REST API in React with thoughtful loading and empty states, plus responsive typography for readability across breakpoints. Impact: Highlights API integration patterns used in technical investigations: validate inputs, handle errors, and surface clear outcomes.

React

REST API

CSS

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Todo app

Problem: Task lists must stay predictable as users add, filter, and reorder items. Approach: Centralized state with Redux and polished motion with Framer Motion—animations reinforce state changes instead of distracting from them. Impact: A compact example of state architecture plus UX details that improve perceived quality and clarity.

React

Redux

Framer Motion

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