Role Overview :

As an AI Development Engineer, you will be responsible for building, deploying, and maintaining AI-powered applications. Your primary mission is to bridge the gap between data science and software engineering, ensuring that AI models are seamlessly integrated into production-ready software using .NET, Java, and Python.

Key Responsibilities :

- Feature Development : Build AI-driven features for both client-facing solutions and internal business applications.

- Integration & APIs : Develop robust APIs (REST/GraphQL) to connect AI models with frontend and backend systems using .NET Core or Java Spring Boot.

- Model Deployment : Wrap ML models into Docker containers and deploy them into production environments.

- Data Orchestration : Work with Snowflake and MS Fabric to manage data pipelines and integrate insights into Power BI.

- Automation : Implement CI/CD pipelines in Azure DevOps to automate the testing and deployment of AI features.

Technical Competencies :

1. Software Engineering (The Foundation) :

- Languages : Strong proficiency in Python (for ML logic) and either .NET Core or Java Spring Boot (for enterprise application logic).

- Web Services : Deep experience in API design and microservices architecture.

2. AI/ML Engineering (The Intelligence) :

- Frameworks : Hands-on experience with Scikit-learn, TensorFlow, or PyTorch.

- Pre-built AI : Expert at implementing OpenAI APIs (LLMs) and Azure Cognitive Services (Vision, Speech, Language).

3. Data & DevOps (The Pipeline) :

- Modern Data Stack : Familiarity with Snowflake, MS Fabric, and data visualization via Power BI.

- Infrastructure : Proficiency with Docker, Kubernetes, and Azure DevOps for automated software delivery.