Published Paper Details:

Technical interviews for software engineering roles demand both deep technical competence and the ability to communicate solutions clearly and confidently. Candidates typically prepare using a patchwork of resources -- coding platforms, textual guides on system design, recorded mock interviews -- none of which provide an integrated, adaptive, and evidence-backed training pipeline. HireIQ is an AI-integrated web platform designed to replace that fragmentation with a single, end-to-end preparation ecosystem. The platform first ingests a candidate's resume and profile and produces a tailored, prioritized skill inventory using layout-aware parsing and transformer-based embeddings. Using that profile, HireIQ generates company- and role-specific roadmaps, micro-projects, and question sequences that combine coding problems, system-design cases, and behavioral prompts. Mock interviews are delivered in both synchronous and asynchronous modes; responses are captured multimodally (audio + transcript + optional video + code / whiteboard sketches) and evaluated by a suite of specialized graders: a sandboxed code judge for correctness and complexity, transformer-based semantic scorers for explanatory answers, and prosody/voice models for delivery and confidence. Session scoring uses temporally aware, window-consistency fusion to stabilize judgments and produce session-level reports with confidence intervals. All automated judgments are supplemented with human-readable evidence (transcript highlights, failing test cases, design checklist gaps) to make feedback actionable. HireIQ also embeds fairness-aware model training and operational privacy controls (consented video capture, encryption, minimal retention) to reduce bias and protect candidates. This document describes HireIQ's architecture, algorithms, evaluation strategy (including data collection, human rater alignment, ablation and fairness studies), sample report templates, and a staged deployment plan. The platform's goals are to (1) accelerate measurable improvement in candidate readiness, (2) provide interpretable, reproducible feedback comparable to expert evaluation, and (3) scale to institutions and training programs while protecting candidate privacy and fairness.