Computer Vision / AI Engineer II

• Applied Model Development: Own the end-to-end development of computer vision and ML models-covering detection, segmentation, classification, OCR and image/video analysis.
• Model Evaluation & Selection: Evaluate model options, benchmark trade-offs, and recommend the right approach for each business problem.
• Training & Fine-Tuning: Train, fine-tune, and optimize models using PyTorch or TensorFlow, including transfer learning and data-efficient techniques.
• Performance Analysis: Define metrics, analyze model performance, diagnose failure modes, and iterate to meet accuracy and latency targets.
• Hardware-Aware Engineering: Account for the real-world constraints of cameras, sensors, lighting, and edge devices that affect data quality and model performance.
• Data Pipelines: Build and maintain data, labeling, and evaluation pipelines that support reliable experimentation and deployment.
• Collaboration: Work with software, hardware, and MLOps engineers to take models from prototype to production.

• Computer Vision (Detection, Segmentation, Classification, OCR)
• Python
• PyTorch and/or TensorFlow
• Machine Learning Model Development & Optimization
• Image & Video Analysis
• Taking Models from Prototype to Production, * 3-5 years of hands-on experience building computer vision and machine learning models.
• Proven track record taking models from experimentation to production.

Skills:

• Proficiency in both modern AI-based CV models (CNNs, transformers, embeddings) and traditional computer vision
• Strong Python skills for CV/ML development, data processing, and experimentation.
• Experience with PyTorch or TensorFlow.
• Hands-on experience with detection, segmentation, classification, and image/video analysis.
• Practical knowledge of computer vision hardware-cameras, sensors, lighting, and edge devices-and the real-world constraints that affect data quality and model performance.
• Experience with broader ML problems: time-series modeling, anomaly detection, clustering, and data analysis.

Abilities:

• Strong analytical and performance-debugging skills.
• Able to evaluate model options and turn business problems into practical AI solutions.
• Strong problem-solving skills and ability to work in a fast-paced, agile environment.

Education:

• Bachelor’s or Master’s degree in Computer Science, Engineering or a related field., * Experience deploying models to edge devices or optimizing for inference (quantization, pruning, TensorRT, ONNX).
• Familiarity with MLOps practices: experiment tracking, model versioning, and drift/performance monitoring.
• Experience with cloud platforms (Azure, AWS, or Google Cloud Platform) and GPU-based training.
• Experience working with retail, IoT, or real-world imaging datasets.