AI Solutions Engineer Overview • The AI Solutions Engineer will design, build, and deploy custom applications and AI solutions for business professionals and practice groups. • Collaborate with stakeholders to identify automation opportunities and deliver iterative solutions enhancing business processes and capabilities. • Utilize modern software development practices to create applications on the Microsoft and Azure stack, ensuring compliance with security standards. • Develop AI solutions, including retrieval-augmented generation assistants and integrations with enterprise AI platforms. • Employ CI/CD pipelines for automated build, test, security scanning, and deployment of applications and services. • Leverage AI-assisted engineering tools to accelerate development while maintaining code quality and security. • Monitor and evaluate deployed applications, ensuring scalability, reliability, and performance over time. • Stay updated on emerging AI technologies and recommend adoption strategies to improve outcomes. AI Solutions Engineer Key Responsibilities & Duties • Design, build, test, deploy, and support custom web applications and APIs for business professionals and practice groups. • Collaborate with network systems and information security teams to ensure solutions comply with governance standards. • Develop and maintain REST APIs and production data pipelines using sound SQL and data modeling practices. • Integrate enterprise AI platforms through APIs and SDKs, building agentic solutions with frameworks and services. • Operate applications and services on Azure, utilizing serverless compute and managed databases. • Follow established software development life cycle methodologies, including Git-based source control and peer code review. • Evaluate and maintain deployed AI solutions, tracking quality metrics such as accuracy, latency, and scalability. • Contribute to team standards and reusable components, ensuring maintainability and security of all code produced. AI Solutions Engineer Job Requirements • Bachelor of Science (BS) degree in a relevant field is required. • 5+ years of professional software engineering experience with C#, ASP.NET MVC, React, and Python. • 2+ years of experience shipping LLM-based applications to production environments. • Proficiency in SQL and data modeling, including production pipelines on SQL Server or similar platforms. • Experience deploying services on Azure or AWS, including containers and serverless computing. • Knowledge of CI/CD pipelines and Git-based workflows is essential. • Preferred experience in regulated industries such as legal or financial services. • Familiarity with AI coding assistants and frameworks for professional development.