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Fullstack Engineer | 14+ yrs Shipping React,Next.js,Node,AI
$50k+
Earned
5x
Hired
4.9
Rating
93
Followers
Fullstack Engineer | 14+ yrs Shipping React,Next.js,Node,AI
Cover image for Led delivery of a cloud-native
Led delivery of a cloud-native K-12 student wellness platform that helps counselors monitor wellbeing, surface at-risk students, and act early—balancing product velocity with FERPA/COPPA-aware design and multi-tenant isolation (API RBAC, PostgreSQL RLS, district-scoped analytics). → Owned technical direction and delivery rhythm across a TypeScript monorepo (pnpm/Turbo): backend API, web app, shared packages, DB migrations, CI/CD gates, and staged deploys to GCP (e.g. Cloud Run), aligning engineering work with roadmap and release risk. → Orchestrated an AI assistant for counselors on Google Vertex AI (Gemini): system prompts, safety settings for K-12, function-calling tool design, multi-round tool loops, and strict separation so UI widgets render from verified tool results—not model hallucinations—with tracing/feedback hooks (e.g. Langfuse) for quality and auditability. → Drove access-control and compliance posture for sensitive student data: role/permission middleware, tenant-scoped queries, and defense-in-depth database policies—documentation and evidence suitable for enterprise security reviews (e.g. SOC 2–style narratives). → Partnered on product and program management: phased feature rollout, PR/branch discipline, and clear ownership of scope vs. risk so pilot schools could onboard without compromising data boundaries. → Shaped integrations and platform boundaries: Firebase auth, Neon PostgreSQL + Drizzle ORM, real-time patterns, BigQuery analytics isolation, and operational practices (testing, lint, type-check) that keep the stack maintainable at scale. → Established a disciplined, AI-accelerated delivery model—not ad-hoc prompting: Cursor rules and Claude skills/agents for repeatable workflows; Husky + lint/type-check/test gates; GitHub Actions CI/CD; Cursor Cloud Agents for automated PR review; and daily monitoring of E2E, coverage, and critical issues—owning AI-orchestrated implementation while enforcing accuracy, consistency, and observability
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155
Full Stack Developer || PHP Backend Developer
5.0
Rating
32
Followers
Full Stack Developer || PHP Backend Developer
Full Stack Developer
6
Followers
Full Stack Developer
Cover image for AI Resume Screening | Candidate
AI Resume Screening | Candidate Ranking System | AI HR Recruiter | ATS CV/Resume Optimization 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 Recruiters often spend hours manually reviewing resumes, comparing candidate qualifications, and identifying the best fit for open positions. To address this challenge, I developed an AI-powered Resume Screening and Candidate Ranking Platform that automates candidate evaluation, improves hiring efficiency, and reduces recruitment time. 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Traditional recruitment processes involve reviewing hundreds of resumes for a single position. This manual approach is time-consuming, inconsistent, and often results in qualified candidates being overlooked. Recruiters needed a solution capable of quickly analyzing resumes, matching them against job requirements, and generating reliable candidate rankings. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 I built an intelligent recruitment platform that leverages Artificial Intelligence and Natural Language Processing (NLP) to automate resume analysis and candidate assessment. 𝗞𝗲𝘆 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: - ATS-compatible resume parsing for PDF and DOCX files - Automated extraction of skills, experience, education, certifications, and contact information - AI candidate matching based on job descriptions - Intelligent candidate scoring and ranking system - Semantic skill matching using NLP techniques - Automated shortlist generation for recruiters - Recruiter dashboard for managing applications and rankings - Bulk resume processing for high-volume recruitment - Interview recommendation system based on candidate fit - Fair and consistent evaluation framework to reduce manual bias 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 The platform was designed with scalability and accuracy in mind. The workflow begins by parsing uploaded resumes and extracting structured candidate data. AI models then compare candidate profiles against job requirements, analyzing technical skills, years of experience, educational background, and industry relevance. A ranking engine generates compatibility scores and presents candidates in order of suitability. Recruiters can review detailed scoring insights, compare applicants, and make faster hiring decisions. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 The solution significantly improved recruitment efficiency and candidate discovery. 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 > Reduced manual resume screening time by up to 80% > Accelerated candidate shortlisting process > Improved recruiter productivity and hiring speed > Increased consistency in candidate evaluation > Enabled processing of hundreds of resumes within minutes > Enhanced talent identification through AI-driven matching 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻 This AI recruitment platform transforms traditional hiring workflows by automating resume screening, ranking candidates intelligently, and helping recruiters identify top talent faster, more accurately, and at scale.
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Experience Customer Service Representative
Experience Customer Service Representative