Operation Supervisor - Annotation

ZestIoT; Hyderabad; On-site; INR 800,000 - 1,200,000; Full time

Job summary

A tech company focused on AI solutions seeks an Operations Supervisor in Hyderabad, India, to manage their data annotation team. The role encompasses overseeing project planning, conducting quality assurance audits, and standardizing processes to enhance productivity. Ideal candidates have 3–5 years of operations management experience, strong digital skills, and a keen attention to detail. This position promises a collaborative environment and the opportunity to streamline crucial operational workflows.

Qualifications

• 3–5 years in Operations Management, ideally as Supervisor or Team Lead in process-heavy environments.
• Comfort working with web-based software applications and troubleshooting.
• Experience in managing workflows, creating trackers, and driving productivity.

Responsibilities

• Manage planning and execution of data annotation tasks.
• Perform quality audits of annotated work, ensuring adherence to standards.
• Document and update Standard Operating Procedures (SOPs) for clarity.

Skills

Operational Management

Digital Literacy

Process Orientation

Attention to Detail

Communication

Tools

Jira

Trello

Excel

Google Sheets

Job description

We build AI-powered turnaround monitoring for airports. Our cameras are installed across parking stands to detect and classify ground turnaround events — chocks, GPU, doors, fuelling, boarding — displayed live on our mobile app and OCC dashboard used by airline operations teams.

We are seeking a process-driven Operations Supervisor to manage our data annotation team. You will own the operational lifecycle of our data—from planning weekly tasks to performing final quality audits. Your primary objective is to ensure that our ML Engineers receive high-quality, accurately labeled datasets on a predictable schedule, allowing them to focus entirely on model development.

Key Responsibilities

• Operational Planning: Breakdown project requirements into daily/weekly annotation tasks. Manage the team's workload to ensure no "idle time" and 100% deadline adherence.
• Quality Assurance (QA): Act as the final gatekeeper for data quality. You will perform daily audits of annotated work, identifying errors and ensuring the team follows established labeling standards.
• Process Standardization: Document and update "Standard Operating Procedures" (SOPs) for the annotator team to reduce ambiguity and improve labeling speed.
• Performance Tracking: Maintain clear dashboards/trackers (Excel/Google Sheets) showing team productivity, error rates, and progress against model training milestones.
• Tool Configuration: Manage the basic setup of projects within our labeling software (defining labels and assigning users).
• Risk Management: Proactively flag potential delays or resource gaps before they impact the development schedule.

Requirements

Must Have

• 3–5 years in Operations Management: Experience as a Supervisor, Team Lead, or Quality Lead in a process-heavy environment (e.g., Data Ops, Content Moderation, or Back-office BPO).
• Proficient Digital Literacy: You must be comfortable working entirely within web-based software applications. You don't need to code, but you should be able to navigate new platforms and troubleshoot basic settings independently.
• Strong Process Orientation: Proven experience in managing workflows, creating trackers, and driving team productivity.
• Attention to Detail: An exceptional ability to spot visual inconsistencies or errors in data.
• Communication: Ability to take complex instructions from developers and translate them into simple, clear guidelines for the annotation team.

Good to Have

• Prior experience with image/video annotation or data labeling projects.
• Experience using project management tools (Jira, Trello, or similar).
• Experience in a Quality Management (QC) or Quality Assurance (QA) focused role.

What Success Looks Like

• Predictable Delivery: Annotation projects are completed on a clear, measurable weekly cadence.
• High Quality Bar: Data rejection rates from the engineering team drop significantly because your internal QA process is robust.
• Developer Freedom: Our developers no longer spend time managing annotators or checking basic labels; they only interact with you for high-level requirements.
• Clear Documentation: There is a clear "source of truth" for how each event should be labeled, reducing team confusion.