AI Solutions Analyst

Compensation Range

$90,854.40 - $127,171.20

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Job Description Summary

The ideal candidate is a hands-on builder with deep knowledge of agentic AI frameworks, Microsoft Copilot Studio, Power Platform automation, and cloud-based AI services. They will serve as the organization's internal expert on AI agent design; identifying high-value automation opportunities, architecting multi-step agentic pipelines, and ensuring responsible deployment of AI solutions.

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Job Description

Agentic AI Design & Automation - 50%

• Design, build, and deploy agentic AI workflows that autonomously execute multi-step business processes using Microsoft Copilot Studio, Power Automate, and Azure AI services.

• Identify and evaluate high-impact automation opportunities across business units, translating complex manual processes into intelligent, agent-driven pipelines.

• Develop custom AI agents and copilots capable of reasoning, tool use, and dynamic decision-making to handle real-world enterprise workflows with minimal human intervention.

• Integrate AI agents with enterprise systems (M365, SharePoint, third-party APIs) to enable seamless end-to-end process automation.

• Leverage large language model (LLM) orchestration patterns including ReAct, chain-of-thought prompting, and tool-calling to build reliable, goal-directed agents.

• Prototype and iterate rapidly on agent designs, validating performance against business outcomes before broader deployment.

Integration & Deployment - 30%

• Partner with IT and business teams to deploy AI agents into production environments, ensuring reliability, security, and scalability.

• Build and maintain agent monitoring frameworks to track performance, detect failures, and trigger human-in-the-loop escalation where appropriate.

• Ensure all agentic solutions adhere to Responsible AI principles and organizational governance standards, including data privacy, fairness, and transparency.

• Stay current with rapidly evolving agentic AI tools, frameworks (e.g., AutoGen, Semantic Kernel, LangChain), and platform capabilities, proactively recommending adoption where appropriate.

• Communicate solution architecture and capabilities clearly to non-technical stakeholders, translating agent behavior into understandable business outcomes.

Documentation & Reporting - 10%

• Maintain thorough documentation of agent architectures, automation logic, integration points, and operational runbooks.

• Prepare executive summaries and operational dashboards that communicate the business impact of deployed AI solutions.

Training & Knowledge Sharing - 10%

• Serve as an internal champion and subject matter expert for agentic AI, conducting workshops and training sessions to build organizational capability.

• Develop reusable agent templates, prompt libraries, and best-practice guides to accelerate adoption across teams.

• Foster a culture of intelligent automation by collaborating with business units to surface new use cases and build a roadmap of AI-powered improvements.

 

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Required Qualifications

Required Education

• Bachelor's Degree or 4 years experience designing and deploying AI/automation solutions

Skills and Abilities

• Agentic AI & LLM Orchestration: Practical experience building autonomous AI agents using frameworks such as Microsoft Copilot Studio, Semantic Kernel, AutoGen, or LangChain, including tool use, memory, and multi-step reasoning.

• Microsoft Power Platform & Copilot: Proficiency in Power Automate, Power Apps, and Copilot Studio for building conversational and process automation solutions at enterprise scale.

• Programming & Scripting: Proficiency in Python for automation, API integration, prompt engineering, and agent logic development. Familiarity with JavaScript or PowerShell is a plus.

• Cloud & AI Services: Hands-on experience with Azure AI Studio, Azure OpenAI Service, Azure Logic Apps, and related cloud services. Familiarity with AWS or GCP AI offerings is beneficial.

• Process Automation Mindset: Strong ability to decompose complex business workflows into automatable steps and design agents that execute reliably in dynamic environments.

• API & Systems Integration: Experience integrating AI agents with REST APIs, enterprise platforms (M365, SharePoint), and data sources.

• Responsible AI & Governance: Understanding of ethical AI practices, data privacy regulations, and organizational security standards as they apply to autonomous AI systems.