Software Engineer II (IN), Senior

Remote Work: Hybrid

• Develop Test Strategies: Formulate comprehensive test strategies for both functional and performance testing within a Continuous Integration (CI) framework, specifically targeting Gen AI and ML-based features/models.
• Application Development: Create and maintain test applications for Gen AI and ML-based features/models across multiple programming environments, including Android Java, C++, and Python.
• Dataset Creation: Generate representative datasets that accurately reflect real-world scenarios to validate model accuracy and performance.
• Model Evaluation: Execute rigorous testing procedures to analyze the functionality and performance of AI and ML models. This includes evaluating object detection, recognition, and OCR capabilities.

• Develop comprehensive test strategies for both functional and performance testing of Generative AI and machine learning-based features/models, with a particular emphasis on computer vision applications.
• Design and implement test applications for Generative AI and machine learning models, including object detectors, recognizers, and OCR systems.
• Create and manage large, representative datasets for model training and validation, ensuring high-quality data annotation and effective dataset management.
• Execute and analyze model functionality and performance, providing detailed insights and recommendations for improvement.

• Application Development Proficiency:
• Strong skills in application development, particularly with SDKs that offer various language bindings such as Android Java and Python.
• Python Scripting Expertise:
• Proficient in Python, with a deep understanding of machine learning, computer vision, data visualization, and data analysis packages.
• ML Testing and Performance Analysis:
• Extensive experience with machine learning testing strategies, including the ability to perform performance analysis of AI models.
• Familiarity with data science principles, including data annotation, visualization, cleaning, statistical analysis, data governance, and security.
• Model Evaluation and MLOps:
• Skilled in evaluating machine learning models, with a solid understanding of model evaluation metrics.
• Experience with MLOps practices and platforms, such as Weights & Biases, to streamline model development and deployment processes.
• Computer Vision Focus:
• Proven experience in computer vision projects, particularly those involving large and complex datasets.
• Ability to manage and annotate large datasets effectively to ensure accurate model validation and performance tuning.

• Prior experience in testing AI solutions in a production environment.
• Experience working with generative AI models and their specific challenges.
• Strong analytical and problem-solving skills, with a keen attention to detail.