Senior Computer Vision Engineer

Design end-to-end computer vision pipelines for object detection, instance/semantic segmentation, and production deployment. Generates training configurations for YOLO/Detectron2/MMDetection, optimizes models for ONNX/TensorRT/OpenVINO runtimes, and builds dataset preparation workflows with format conversion and augmentation.

Core Capabilities

• Detection pipeline design — requirements analysis, architecture selection (YOLO/RT-DETR/Faster R-CNN/DINO), dataset prep, training config, and metric evaluation.
• Model optimization & deployment — baseline benchmarking, ONNX export, INT8/FP16 quantization, and conversion to TensorRT/OpenVINO/CoreML/TFLite per target platform.
• Dataset engineering — audit, cleaning, format conversion (COCO/YOLO/VOC/CVAT/LabelMe), augmentation config, and stratified train/val/test splits.
• Architecture guidance — detection and segmentation architecture trade-offs plus CNN vs Vision Transformer selection.
• Production targets — FPS, mAP, latency P99, memory, and model-size budgets for real-time, high-accuracy, and edge deployments.

When to Use

• Building an object detection or segmentation system from scratch.
• Optimizing and deploying a trained model to GPU, edge, or mobile.
• Preparing, converting, or auditing a computer vision dataset.
• Choosing an architecture for a speed/accuracy/deployment trade-off.

Clarify First

Before generating training configs or pipelines, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Task — detection / instance or semantic segmentation / classification (selects the architecture and --task)
• Dataset — location and format (COCO / YOLO / VOC) to analyze or convert (the input to dataset_pipeline_builder.py)
• Deployment target — GPU / edge / mobile (drives architecture choice and inference_optimizer --target)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Tools

Tool	Purpose	Command
vision_model_trainer.py	Generate training configs for YOLO / Detectron2 / MMDetection	python scripts/vision_model_trainer.py data/coco/ --task detection --arch yolov8m -o configs/train.yaml
inference_optimizer.py	Analyze, benchmark, and recommend optimizations for a model	python scripts/inference_optimizer.py model.pt --analyze --benchmark --recommend --target edge
dataset_pipeline_builder.py	Analyze/convert/split/augment/validate CV datasets (subcommands)	python scripts/dataset_pipeline_builder.py analyze --input data/coco/

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/detection-workflows.md — quick-start commands and the three end-to-end workflows (detection pipeline, model optimization/deployment, dataset prep) plus the architecture selection guide. Read when executing a pipeline.
• references/commands-targets-and-troubleshooting.md — framework command catalogs (YOLO/Detectron2/MMDetection/optimization), performance targets, anti-patterns, troubleshooting table, and success criteria. Read while running training or deployment.
• references/tool-reference.md — full parameter, example, and output-format reference for the three scripts. Read when scripting the tools.
• references/computer_vision_architectures.md — CNN backbones (ResNet, EfficientNet, ConvNeXt), ViT variants (ViT, DeiT, Swin), detection heads, and FPN/BiFPN/PANet necks. Read when choosing or tuning architectures.
• references/object_detection_optimization.md — NMS variants, anchor optimization, loss design (focal, GIoU/CIoU/DIoU), training strategies, and detection augmentation. Read when improving detection accuracy.
• references/production_vision_systems.md — ONNX/TensorRT export, batch inference, edge deployment (Jetson, Intel NCS), Triton serving, and video pipelines. Read when deploying to production.

Scope & Limitations

This skill covers:

• End-to-end object detection and segmentation pipeline design (data preparation through production deployment)
• Training configuration generation for Ultralytics YOLO, Detectron2, and MMDetection frameworks
• Model optimization and export to ONNX, TensorRT, OpenVINO, and CoreML runtimes
• Dataset format conversion (COCO, YOLO, Pascal VOC, CVAT), splitting, validation, and augmentation configuration

This skill does NOT cover:

• Generative vision tasks (image generation, style transfer, super-resolution) -- see dedicated generative AI skills
• 3D reconstruction, SLAM, or point cloud processing beyond basic depth estimation
• Medical imaging regulatory compliance (DICOM, FDA 510(k)) -- see ra-qm-team/ compliance skills
• Real-time video streaming infrastructure (RTSP, WebRTC, GStreamer pipeline design) -- see senior-devops for infrastructure

Integration Points

Skill	Integration	Data Flow
senior-ml-engineer	Model serving and MLOps pipeline setup	Trained model artifacts (.pt, .onnx) flow into model_deployment_pipeline.py for containerized serving and monitoring
senior-data-engineer	Dataset ETL and storage pipelines	Raw image data ingested via pipeline_orchestrator.py; cleaned datasets flow into dataset_pipeline_builder.py for CV formatting
senior-data-scientist	Experiment design and statistical analysis	Experiment parameters from experiment_designer.py guide hyperparameter search; model metrics feed back for significance testing
senior-devops	CI/CD and GPU infrastructure provisioning	Optimized model artifacts deployed via CI/CD pipelines; GPU node scaling managed through infrastructure-as-code
senior-prompt-engineer	Multimodal RAG and vision-language integration	Vision model embeddings and detections feed into rag_system_builder.py for multimodal retrieval pipelines
senior-cloud-architect	Cloud GPU resource planning and cost optimization	Benchmark results from inference_optimizer.py inform instance type selection and auto-scaling policies