Project Delivery - Intern

About the Role Arctic Engine is hiring 3 Project Delivery Interns to drive end-to-end execution of data annotation projects. You will own quality, build our gig annotator community, create SOPs, train workforce, and ensure SLA delivery — with hands-on annotation work whenever production demands it. What You'll Do AI Evangelist in project delivery * Leverage AI to create QC tools and workflows * Identify open-source or economical AI softwares for annotation and data recording Quality Control * Run sample-based and 100% QC audits as per project tier * Track inter-annotator agreement (IAA), accuracy, and rework rates * Maintain error logs and feedback loops with annotators SOP \& Process Building * Translate client guidelines into annotator-friendly SOPs with edge cases and visual examples * Build playbooks for onboarding, escalation, and delivery * Identify automation/tooling opportunities to reduce TAT Gig Workforce \& Training * System driven (AI) for workforce management * Source, screen, and onboard digital gig annotators across India * Run paid pilot/qualification tests before deployment * Deliver training, calibration sessions, and refreshers Community Building * Manage gig workforce on WhatsApp/Discord/Telegram channels * Drive engagement, retention, and grievance resolution * Build leaderboards, recognition, and tiering systems Project Delivery \& SLA * Track daily throughput, accuracy, and utilization vs. SLA * Flag risks early; jump into annotation tasks personally when ramp-up lags * Coordinate with PMs and client SPOCs on delivery status Must-Haves* Bachelor's (final year) or fresh graduate — any stream * Strong English written communication * Detail-oriented with high ownership; comfortable with ambiguity and using AI * Hindi \+ 1 regional language preferred: Tamil, Telugu, Urdu, Malayalam, Marathi, or Odia Good-to-Have* Exposure to CVAT, Label Studio, LabelBox, V7, or similar tools * Prior experience in BPO/KPO, content moderation, or community management * Basic understanding of ML/AI data pipelines