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 • 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.