AI Engineer

The Senior AI Engineer will serve as the technical lead of a multidisciplinary team to design, deliver, and operationalize AI solutions that are simple, standard, secure, and cost-effective. The role involves translating business needs into AI applications using Azure AI Services, Databricks, and Power Platform to ensure measurable business value, leveraging reusable reference architectures and low-code solutions., * Translate business use-cases into optimal application solution architectures prioritizing simplicity, security, standardization, and re-use, in collaboration with architects and subject matter experts.

• Analyze, design, and develop solutions based on Azure AI services.
• Coordinate technical implementation of solutions.
• Coach and advise co-workers on a technical level.
• Define and maintain reference architectures and coding standards.
• Deploy and maintain infrastructure in a structured and standardized way via reusable modules.

• Analytical, problem solver, solution-minded.
• Fast learner and eager to grow.
• Team management and spirit.
• Good communication skills.
• Pro-active and entrepreneurial.
• Business case development, estimating the technical cost for development and maintenance of AI solutions.
• Familiarity with working methodologies such as Scrum/Kanban, test-driven development, and continuous integration.

Technical Skills:

• Technical knowledge of the Azure cloud platform, including networking and security, and experience implementing solutions using Azure AI services: Azure Document Intelligence, Azure OpenAI, Azure AI Search, and Azure AI Foundry.
• Expertise in Generative AI development and common building blocks, such as LLMs, RAG, prompt engineering, chunking & indexing, and search & ranking techniques.
• End-to-end development of both back-end and front-end (user interfaces & user experience) of use cases.
• High-level knowledge of the Power Platform, including Power Automate, Power Apps (Canvas and model-Driven Apps), and embedded AI services.
• High-level knowledge of Databricks, including Mosaic AI, data pipelines using PySpark and SQL, orchestration of data pipelines using Airflow.
• Experience with infrastructure-as-code through Terraform for the deployment of Azure infrastructure.
• Solution architecture: comprehensive overview, insight to select the right technology per component, with a trade-off between best fit and integration needs.