US Solution Engineer, Pointclickcare

• 2–5+ years of production experience in SQL, C#, Python, or JavaScript
• Strong relational database design, scripting, and query optimization skills
• Experience integrating AI or LLM services, with exposure to frameworks like Semantic Kernel, Copilot Studio, or AI Foundry
• Demonstrated ability to build scalable, repeatable solutions and move from concept to production in ambiguous environments
• Strong problem-solving skills in ambiguous environments with the ability to work seamlessly across product, integration, and delivery teams
• Strong Cross functional work experience with Engineering, Implementations, or Security teams
• Experience building and deploying applications in the Microsoft Azure ecosystem
• Familiarity with CI/CD pipelines and structured Git workflows

Desirable

• Reporting into the Sr Manager, Technical Services
• We are looking for a Solutions Engineer to design and implement database-driven, AI-enabled workflows that reduce manual implementation effort, accelerate customer onboarding, and improve data accuracy and reliability across our healthcare SaaS platform
• This role focuses on enabling scalable, repeatable delivery models through structured automation
• You will translate architectural concepts into production-ready systems that automate operational work using the Microsoft Azure ecosystem
• This is a hands-on engineering role centered on building durable automation solutions, not experiments
• Design and implement database-driven operational workflows that automate data transformation, validation, and configuration processes. Integrate LLM or AI services into structured workflows where appropriate, enabling autonomous processes that can be triggered, executed, logged, and monitored reliably
• The objective is to reduce manual intervention across implementation and support operations while improving efficiency and consistency
• Partner with solution architects to convert technical designs into scalable, maintainable implementations. Establish structured data contracts and schemas to ensure clarity and consistency across systems
• Implement appropriate guardrails, logging, monitoring, and observability practices to support operational stability. Ensure workflows meet security, compliance, and production-readiness standards prior to deployment
• Leverage Microsoft Azure services (Functions, App Services, Logic Apps, Azure AI) to design solutions that are modular, version-controlled, and CI/CD enabled for continuous improvement
• Integrate LLMs into structured database workflows and support retrieval-based (RAG) patterns. Implement evaluation and monitoring loops to ensure intelligent services improve execution quality without adding noise