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Job Summary We are looking for a detail-oriented Data Annotation Engineer with experience in image and LiDAR annotation to support AI and Computer Vision initiatives for industrial asset intelligence. The ideal candidate should have hands-on expertise in annotating image and point cloud datasets, ensuring high-quality training data for machine learning models used in industrial inspection, defect detection, and digitalization of power infrastructure. The candidate should possess strong analytical skills, a quality-focused mindset, and experience working with industry-standard annotation tools. Key Responsibilities - Perform high-quality image and LiDAR dataset annotation following project guidelines. - Annotate industrial assets using predefined taxonomies and class definitions. - Execute image annotation techniques including Bounding Boxes, Polygons, Semantic Segmentation, Keypoints, and Image Classification. - Perform 2D and 3D LiDAR point cloud annotation, Cuboid Annotation, and Object Tracking. - Identify and classify defects in industrial equipment and infrastructure. - Ensure annotation consistency, completeness, and accuracy across all assigned datasets. - Participate in quality reviews and implement QA feedback. - Meet productivity targets and turnaround time (TAT) requirements. - Maintain annotation metadata and project documentation. - Collaborate with QA teams and project leads to improve annotation quality. Required Technical Skills Image Annotation - Bounding Box Annotation - Polygon Annotation - Semantic Segmentation - Image Classification & Tagging - Keypoint Annotation - Object Detection LiDAR Annotation - Point Cloud Annotation - Cuboid Annotation - Object Tracking - 2D & 3D Annotation - LiDAR Asset Classification - Geospatial Annotation Industrial Asset Annotation Experience in annotating or identifying: - Power Transformers - Electrical Substations - Switchgear - Transmission Towers - Electrical Components - Industrial Equipment - Defect Detection & Classification Preferred Annotation Tools Experience with one or more of the following: - CVAT - Label Studio - Supervisely - Labelbox - V7 Darwin - Scale AI - Roboflow - Enterprise Image or LiDAR Annotation Platforms Required Qualifications - 3 6 years of experience in Image and/or LiDAR Annotation projects. - Hands-on experience with image and point cloud annotation tools. - Knowledge of industrial asset annotation is preferred. - Ability to understand and follow annotation guidelines and class definitions. - Strong attention to detail with a focus on annotation quality and consistency. - Ability to meet quality, productivity, and turnaround time (TAT) targets. - Willingness to complete project onboarding and quality training before deployment. Preferred Skills - Basic understanding of Computer Vision and Machine Learning workflows. - Familiarity with industrial equipment used in power generation, transmission, and distribution. - Exposure to AI data preparation, defect detection, or digital inspection projects. - Robust analytical and problem-solving skills. - Excellent communication and teamwork abilities. Key Deliverables - Fully annotated image and LiDAR datasets. - Annotation metadata documentation. - Quality Assurance (QA) reports. - Productivity reports. - Turnaround Time (TAT) reports. - Reworked datasets based on QA feedback. Employment Type Full-Time / Contract (Project-Based) Industry Artificial Intelligence | Computer Vision | Industrial Automation | Data Annotation | Energy & Utilities Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.