AI/ML Data Supervisor in Bengaluru, India

AiDASH is leading the PreventionFirst™movement for electric utilities and transforming grid resilience through its pioneering platform that unifies vegetation, asset, storm, and wildfire intelligence. Powered by SatelliteFirst™ Inspection & Monitoring, AiDASH delivers comprehensive visibility across the entire grid at the right frequency and budget, using the right data modality. In 2026, Forbes named AiDASH one of America's Best Startup Employers for the 4th consecutive year, and TIME included AiDASH among America's Top GreenTech Companies for the 3rd year in a row. Deloitte Technology Fast 500™ ranked AiDASH No. AiDash is looking for an experienced Manager – AI Data Ops to lead a team responsible for sourcing, processing, and annotating satellite and remote sensing imagery that powers our AI/ML models. This role blends hands-on GIS expertise with strong people management and vendor coordination skills, and is central to ensuring high-quality, timely , and scalable geospatial data pipelines.
The ideal candidate has deep experience working with satellite and aerial imagery, understands the nuances of remote sensing data sourcing, and has successfully managed both in-house annotation teams and external vendor partners.

GIS & Remote Sensing Operations

• Lead end-to-end sourcing of satellite imagery, aerial data, and remote sensing datasets from various providers (e.g., optical, SAR, multispectral, hyperspectral sources).
• Oversee image annotation and labeling workflows for use cases such as land cover classification, vegetation/asset monitoring, infrastructure mapping, and change detection.
• Ensure annotation accuracy, consistency, and adherence to defined quality standards (QA/QC frameworks) across all delivered datasets.
• Define and continuously improve annotation guidelines, taxonomies, and labeling protocols in collaboration with Data Science and ML teams.
• Stay current with GIS tools, remote sensing platforms, and annotation technologies (e.g., QGIS, ArcGIS, ERDAS, Google Earth Engine, CVAT, Labelbox, or similar).

Manage, mentor, and grow a team of GIS analysts/annotators (team size: specify, e.g., 15–40+), including performance reviews, capacity planning, and skill development.

• Build a strong bench of GIS talent through structured hiring, onboarding, and training programs.
• Drive productivity, quality, and throughput targets across the team through KPIs and process discipline.
• Identify, evaluate, and onboard third-party vendors/BPOs for image annotation and data labeling at scale.
• monitor ongoing performance against these agreements.
• Act as the primary point of contact for vendor escalations, quality issues, and capacity scaling needs.
• Coordinate cross-functionally with Data Science, Engineering, Product, and Delivery teams to align annotation output with project timelines and model requirements.

Build dashboards/reports to track annotation throughput, accuracy metrics, vendor performance, and cost efficiency.

~ Manage budgets related to data sourcing, annotation operations, and vendor contracts.

8+ years of overall experience in GIS, remote sensing, or geospatial data operations; Strong hands-on knowledge of satellite imagery sourcing (optical, SAR, multispectral) and remote sensing data pipelines.

• Proven experience managing image annotation/data labeling teams and processes at scale.
• Demonstrated experience handling third-party vendors/BPOs — including contracting, SLA management, and performance monitoring.
• Proficiency with GIS software (ArcGIS, QGIS) and familiarity with remote sensing platforms (Google Earth Engine, ERDAS Imagine, ENVI, etc.).
• Track record of driving automation heavily in previous roles — replacing manual/repetitive steps with tools, scripts, or AI-assisted workflows.
• An "AI-native" way of working — comfortable using LLMs/AI copilots as part of daily workflow (analysis, documentation, communication, process design), not just as a novelty.
• Experience working with AI/ML teams and understanding of how annotated data feeds into model training.
• Strong understanding of quality frameworks (QA/QC) for spatial data and annotation accuracy.
• Bachelor's/Master's degree in Geography, GIS, Remote Sensing, Geoinformatics, Environmental Science, or a related field.

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