TECHNICAL SKILLS AND METHODS

Full-Stack Web Development
- Frontend Frameworks: Built a responsive SPA using React (Vite) with React Router, modular components, and hooks-based state management for interviews and practice flows
- UI/UX Implementation: Implemented modern, mobile-friendly layouts (hero, benefits grid, problem cards, voice interface) with custom CSS and intuitive navigation.
- State & Session Flows: Designed page and component structure (Home, Prepare, Technical, Behavioral) to support clear user journeys for technical and behavioral interview practice.

Backend & API Engineering
- RESTful API Design: Developed Express.js server with clearly defined endpoints for session lifecycle and voice processing (e.g., POST /api/session, POST /api/transcribe).
- File & Audio Handling: Integrated Multer to accept browser-recorded audio (WebM, WAV, MP3) and process multipart/form-data uploads.
- Session Management: Implemented in-memory session store for tracking interview progress, question sequencing, and per-session context

AI & External Service Integration
- Speech-to-Text Integration: Connected ElevenLabs API to convert user-recorded audio into text while handling varying formats and recording quality.
- AI Feedback Workflows: Integrated Vellum AI workflows to generate structured interview feedback, including STAR-style guidance for behavioral questions.
- Secure API Configuration: Managed external API keys via environment variables and configured CORS for secure client–server communication.

Interview Experience Design
- Technical Interview Roadmap: Curated LeetCode-linked coding problems with difficulty filters (Easy/Medium/Hard) to mirror real technical screening expectations.
- Behavioral Training Flows: Designed voice-based behavioral practice with common interview questions, STAR method education, and iterative feedback loops.
- Voice Practice Workflow: Defined end-to-end experience from recording to receiving AI feedback and progressing through a structured session.Architecture, Deployment & Collaboration
- Project Architecture: Organized a clear client/server monorepo with dedicated folders for components, pages, questions, sessions, and AI integration logic.
- Environment & Deployment: Implemented dev and production workflows (Vite build, Node server), environment-specific config, and guidance for logging, monitoring, and future DB integration.
- Version Control & Teamwork: Collaborated in a multi-contributor GitHub repo with branches and PR-based workflow to maintain code quality and shared ownership.

Problem Statement

Technical grads and university students are academically strong but consistently fail to convert their skills into offers because they are professionally unprepared for today’s AI-driven, hyper-competitive job market. Recruiters see candidates who know the theory yet cannot present targeted portfolios, pass ATS filters, or perform confidently in modern interview formats.

Despite years of education, many candidates:

Struggle to translate coursework into company-relevant projects aligned with a specific tech stack, leading to portfolio misalignment with real hiring needs.
Get filtered out by ATS systems for missing role-specific keywords, frameworks, and project signals, often without understanding why their applications are rejected.
Enter interviews without structured, repeatable practice, resulting in poor communication, anxiety under pressure, and difficulty showcasing their true skill level.

At the same time, the interview process itself often behaves as a gatekeeping mechanism rather than a fair assessment of ability, disproportionately penalizing candidates who have not trained on interview-specific patterns, whiteboard-style problems, and behavioural storytelling frameworks like STAR. As a result, qualified students underperform relative to their actual capability, reinforcing a broken system that rewards interview preparation rather than real potential

Problem Statement

Despite years of education, many candidates:

Struggle to translate coursework into company-relevant projects aligned with a specific tech stack, leading to portfolio misalignment with real hiring needs.
Get filtered out by ATS systems for missing role-specific keywords, frameworks, and project signals, often without understanding why their applications are rejected.
Enter interviews without structured, repeatable practice, resulting in poor communication, anxiety under pressure, and difficulty showcasing their true skill level.

SOLUTION

finalinterview is an AI-powered interview preparation platform that bridges the gap between academic readiness and real job acquisition by transforming generic prep into targeted, company-specific practice. It uses AI to analyze job postings, employee profiles, and company tech blogs to identify the exact programming languages, frameworks, and tools used by a candidate’s dream companies, then generates tailored portfolio pieces and coding challenges that mirror those real stacks and problems. The platform combines voice-based technical and behavioral mock interviews with ElevenLabs transcription, Vellum coaching, and Gemini analysis to deliver real-time feedback on clarity, structure, STAR usage, and delivery so students can iterate until they perform at interview-ready level, not just study-level.