AI Development Engineer

As an AI Development Engineer, your role involves building, deploying, and maintaining AI-powered applications. You will bridge the gap between data science and software engineering by integrating AI models into production-ready software using .NET, Java, and Python. Key Responsibilities: - Feature Development: You will build AI-driven features for client-facing solutions and internal business applications. - Integration & APIs: Developing robust APIs (REST/GraphQL) to connect AI models with frontend and backend systems using .NET Core or Java Spring Boot. - Model Deployment: Wrapping ML models into Docker containers and deploying them into production environments. - Data Orchestration: Collaborating with Snowflake and MS Fabric to manage data pipelines and integrate insights into Power BI. - Automation: Implementing CI/CD pipelines in Azure DevOps to automate the testing and deployment of AI features. Qualifications Required: - Strong proficiency in Python for ML logic and either .NET Core or Java Spring Boot for enterprise application logic. - Deep experience in API design and microservices architecture. - Hands-on experience with frameworks like Scikit-learn, TensorFlow, or PyTorch. - Expertise in implementing OpenAI APIs (LLMs) and Azure Cognitive Services (Vision, Speech, Language). - Familiarity with Snowflake, MS Fabric, and data visualization via Power BI. - Proficiency with Docker, Kubernetes, and Azure DevOps for automated software delivery. As an AI Development Engineer, your role involves building, deploying, and maintaining AI-powered applications. You will bridge the gap between data science and software engineering by integrating AI models into production-ready software using .NET, Java, and Python. Key Responsibilities: - Feature Development: You will build AI-driven features for client-facing solutions and internal business applications. - Integration & APIs: Developing robust APIs (REST/GraphQL) to connect AI models with frontend and backend systems using .NET Core or Java Spring Boot. - Model Deployment: Wrapping ML models into Docker containers and deploying them into production environments. - Data Orchestration: Collaborating with Snowflake and MS Fabric to manage data pipelines and integrate insights into Power BI. - Automation: Implementing CI/CD pipelines in Azure DevOps to automate the testing and deployment of AI features. Qualifications Required: - Strong proficiency in Python for ML logic and either .NET Core or Java Spring Boot for enterprise application logic. - Deep experience in API design and microservices architecture. - Hands-on experience with frameworks like Scikit-learn, TensorFlow, or PyTorch. - Expertise in implementing OpenAI APIs (LLMs) and Azure Cognitive Services (Vision, Speech, Language). - Familiarity with Snowflake, MS Fabric, and data visualization via Power BI. - Proficiency with Docker, Kubernetes, and Azure DevOps for automated software delivery.