Diligent Corporation is hiring a Senior Software Engineer [AI Solutioning]

Here’s a summary of the role: Ready to build real-world AI solutions that actually make an impact? You’ll take cutting-edge AI and GenAI concepts—like copilots, agents, and intelligent workflows—and turn them into scalable, production-ready systems used across sales, finance, customer success, and more. If you enjoy hands-on backend engineering, working with APIs, cloud platforms, and LLMs, and transforming ideas into high-performing AI-driven products, this role puts you right at the center of innovation. Here’s a breakdown of what you’ll do (not all of it, just the important stuff): - Build and scale backend services, APIs, and integrations that embed AI/GenAI into enterprise systems (CRM, ERP, BI) - Develop LLM-powered solutions such as RAG pipelines, copilots, agents, and automation workflows - Take AI solutions from concept to production—design, build, test, deploy, and monitor - Collaborate with architects and business stakeholders to turn requirements into practical AI implementations - Create and manage data pipelines to enable reliable, high-quality AI workflows - Ensure solutions are secure, scalable, observable, and aligned with best practices These are the essentials you’ll need to get an interview: - 5+ years of experience in software or solutions engineering with production systems - Strong programming skills in Python, Java, C#, or Node.js - Hands-on experience with APIs, integrations (REST/webhooks), and backend development - Practical experience working with LLMs/GenAI tools (e.g., OpenAI, LangChain, or similar) - Experience with cloud platforms (preferably AWS), CI/CD, and containerization - Solid understanding of AI/GenAI concepts like prompt engineering, RAG, or agents - Ability to translate technical solutions into business impact and collaborate across teams It would be great if you had these too, but we’ll support you if you don’t: - Experience integrating AI into CRM, ERP, or analytics platforms - Familiarity with MLOps/LLMOps concepts such as model lifecycle and monitoring - Exposure to machine learning or data analytics tools (e.g., Scikit-learn, dashboards) - Experience working in global or distributed teams - Knowledge of AI governance, security, and responsible AI practices