Senior Software Engineer - AI/ML Developer with C

You are a Senior AI/ML Engineer with strong expertise in C# and Azure DevOps, responsible for driving the implementation and operation of a training model analytics platform. This platform focuses on evaluating conversational AI and AI assistants to provide actionable insights and ensure model accuracy and trustworthiness. Your primary responsibilities include leading the development and operation of a scalable analytics platform using C# .NET and Azure cloud services. You will architect, implement, and maintain backend solutions for robust data processing and analytics. Your role also involves integrating machine learning model evaluation techniques into the analytics pipeline, particularly focusing on conversational AI performance and reliability. Collaboration with cross-functional teams such as AI/ML engineers, data scientists, and product managers is essential to deliver platform features and improvements. Additionally, ensuring adherence to best practices in software engineering, security, and cloud infrastructure is crucial. You will be responsible for translating data science and model evaluation results into clear, actionable analytics and reports. In terms of technical expertise, you should have extensive professional experience in C# .NET (Core and Framework). Advanced knowledge of Azure DevOps, including CI/CD pipelines, infrastructure as code, cloud monitoring, and scalable deployments is required. A proven track record in designing and maintaining enterprise-grade backend solutions is essential. Experience with containerization using tools like Docker and Kubernetes is also beneficial. Your AI/ML & Data Science Knowledge should include a working understanding of machine learning and model evaluation techniques. Familiarity with k-fold cross-validation, precision, recall, F1 score, and other relevant metrics is expected. Experience in analyzing and benchmarking model performance across multiple datasets is valuable. An understanding of AI trustworthiness principles, encompassing model accuracy, interpretability, and robustness, is necessary. Furthermore, you should be able to translate complex ML evaluation insights into actionable platform features.,