Work Description

- Train and fine-tune instance segmentation and object detection models (Mask R-CNN, YOLO-family) to detect irrigation and landscape symbols across mixed symbol libraries, varying scales and rotations

- Build the hatch and bed-line segmentation pipeline that measures areas for sod, seed, mulch, paving and similar, including cases where identical hatches carry different labels or appear nowhere in the legend

- Design the tiling and preprocessing strategy for large-format plan sheets, and own the trade-off between tile count, detection recall and inference cost

- Implement calibrated confidence scoring, and the unknown — needs review” path so unfamiliar symbols surface instead of being misclassified with false confidence

- Run the three fine-tuning rounds: train, evaluate against acceptance criteria, error-analyse which classes underperform, and carry a written remediation plan into the next round

- Deploy models to Amazon SageMaker asynchronous endpoints with scale-to-zero,



and keep releases immutable and version-tagged so any release can be rolled back

- Work with the QA engineer on per-class acceptance measurement, no-regression checks and the calibration and held-out split

Skills required

- Solid Python, and production experience with PyTorch on detection or segmentation tasks — not just notebook experiments

- Hands-on work with Mask R-CNN, YOLO, Detectron2 or similar, including training on custom datasets and diagnosing class-level failure

- OpenCV and image preprocessing: tiling, rotation, scale normalisation, morphology

- PDF and raster handling at scale (PyMuPDF, pdf2image, Poppler, Pillow)

- A working understanding of detection metrics — mAP, IoU, precision and recall per class — and why aggregate accuracy hides per-class regression

AWS: S3, SQS, and either SageMaker or comparable managed inference

📌 AI Engineer Computer Vision- Bangalore location (Bengaluru)
🏢 V2Soft
📍 Bengaluru