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