chartis - AI Solutions Engineer
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Requirements
• 3+ years of recent experience building AI agents or automation solutions (startup, consulting, or enterprise) • Hands-on experience with ChatGPT, Microsoft Copilot Studio and Microsoft Power Platform. • Experience working with low-code/no-code tools to deliver technical solutions. Prior experience with Python (or similar) preferred. • Familiarity with Azure cloud services, including M365, SharePoint, Azure OpenAI and identity/access management concepts. • Understanding of prompt engineering, evaluation frameworks, and AI governance considerations. • Ability to operate independently and manage projects from requirements gathering through delivery. • Excellent verbal and written communication skills, with the ability to translate business needs into technical designs. • Experience in healthcare, healthcare data, or regulated industries preferred but not required. • Salary range: $135,000 - $155,000, plus may be eligible for an annual discretionary bonus. The salary range for this role takes into account the wide range of factors that are considered in making compensation decisions including, but not limited to, skills, experience, training, licensure and certifications, practice area, and other business and organizational needs. In addition, Chartis offers several benefits including medical, dental, vision, HSA, FSA, disability insurance, life insurance, 401(k) match, paid time off, wellness stipend, and additional voluntary benefits.
Responsibilities
• Design, build, and deploy AI assets using Copilot Studio and ChatGPT, including prompts, agents, skills, knowledge sources, and integrations. • Support operational platform management and configuration for AI tools such as ChatGPT, Claude, and Microsoft Copilot. • Implement integrations between AI platforms and enterprise systems, including structured data platforms, document repositories, APIs, and cloud environments. • Establish and execute testing frameworks for AI solutions, including functional validation, prompt evaluation, edge-case testing, and regression testing to ensure reliability and performance over time. • Monitor deployed agents by tracking utilization, performance metrics, and user adoption; systematically review outputs to ensure accuracy, alignment with business intent, and responsible AI standards. • Operationally manage role-based access controls and environment configurations for AI solutions and Copilot Studio deployments. • Expand solutions beyond low-code tooling (where appropriate) by developing code-first AI services and agents using Python and Azure-based services, ensuring scalability, extensibility, and long-term maintainability.
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