AI Engineer

Your role at GEI. The AI Engineer is responsible for the development of AI solutions, typically leveraging pretrained models and copilots, to support GEI s priority digital and AI initiatives. This role focuses on building, deploying, and integrating AI capabilities into business workflows in a secure, scalable, and maintainable manner. The AI Engineer plays a hands-on role in active AI Solutions Factory use cases by implementing AI-based solutions, integrating enterprise data sources, and supporting solution reliability and performance. This role works closely with solution architects and platform teams to ensure AI solutions are production-ready, aligned with GEI standards, and capable of scaling across use cases. Essential Responsibilities & Duties Build and deploy AI-based solutions using pretrained models and Copilot technologies. Design and implement prompt flows, plugins, and orchestrations using Copilot Studio and Azure Functions. Integrate AI capabilities into business workflows and applications. Integrate enterprise data sources securely, including APIs, Graph connectors, retrieval-augmented generation (RAG), and event-driven patterns. Maintain and optimize Power BI dashboards that support AI-enabled workflows and insights. Instrument AI solutions for telemetry, reliability, performance monitoring, and cost control. Provide technical support and troubleshooting for deployed AI solutions. Identify implementation risks and support mitigation strategies in collaboration with architecture and platform teams. Minimum Qualifications 4+ years of software engineering experience, with at least 1 year building Generative AI applications. Experience with Generative AI Large Language Models (LLMs), including solution development and fine-tuning for domain-specific tasks. Proficiency in at least one programming language such as Python, PySpark, R, or SQL. Experience delivering Generative AI (LLM) solutions, preferably on Azure. Familiarity with Azure AI library APIs, including GPT, Codex, and DALL E, and other frameworks (e.g., Databricks Mosaic) for integrating Generative AI into business workflows. Knowledge of big data technologies such as Spark and Databricks; familiarity with TensorFlow and PyTorch is a plus. Knowledge of Azure cloud services, including Azure AI Platform, Azure Data Factory, Azure Synapse, and Azure Cognitive Services, and their integration with ML workflows. Familiarity with AI ethics, bias mitigation, explainability techniques, and responsible AI practices. Knowledge of security best practices for AI solutions, including data encryption, access control, and endpoint protection. Prior experience implementing AI/ML solutions in professional services, engineering, or construction environments is a plus. Azure or Databricks certifications (e.g., Azure AI Engineer Associate, Azure Solutions Architect Expert, Databricks ML Professional, Databricks Data Engineer Professional) are a plus. Physical Requirements WORK ENVIRONMENT Functional Demands Desired Candidate Profile 4+ years of software engineering experience, with at least 1 year building Generative AI applications. Experience with Generative AI Large Language Models (LLMs), including solution development and fine-tuning for domain-specific tasks. Proficiency in at least one programming language such as Python, PySpark, R, or SQL. Experience delivering Generative AI (LLM) solutions, preferably on Azure. Familiarity with Azure AI library APIs, including GPT, Codex, and DALL E, and other frameworks (e.g., Databricks Mosaic) for integrating Generative AI into business workflows. Knowledge of big data technologies such as Spark and Databricks; familiarity with TensorFlow and PyTorch is a plus. Knowledge of Azure cloud services, including Azure AI Platform, Azure Data Factory, Azure Synapse, and Azure Cognitive Services, and their integration with ML workflows. Familiarity with AI ethics, bias mitigation, explainability techniques, and responsible AI practices. Knowledge of security best practices for AI solutions, including data encryption, access control, and endpoint protection. Prior experience implementing AI/ML solutions in professional services, engineering, or construction environments is a plus. Azure or Databricks certifications (e.g., Azure AI Engineer Associate, Azure Solutions Architect Expert, Databricks ML Professional, Databricks Data Engineer Professional) are a plus.