AI and Automation Engineer

**PLEASE CAREFULLY READ ALL THE DETAILS BEFORE APPLYING***

Job Title: AI & Automation Engineer
Rate: TBD
Work Type: Remote, Full-time
Working Hours: Mon–Fri: 8am – 5pm CST
Start Date: TBD


JOB OVERVIEW:

The AI & Automation Engineer is responsible for the strategic design, development, and operational management of automation solutions, data pipelines, and AI-powered integrations. This role drives operational efficiency and business intelligence by leveraging modern tools including MCP servers, Claude AI, and Microsoft Copilot to deliver scalable, intelligent automation. The AI & Automation Engineer fosters a culture of innovation, accountability, and continuous improvement, while ensuring compliance with company policies and industry best practices.


JOB ROLE & RESPONSIBILITIES:

Strategic Leadership
-Define and execute the strategic roadmap for automation and data engineering in alignment with company goals.
-Partner with business and IT leadership to develop scalable automation and data solutions.
-Monitor industry trends and incorporate best practices to maintain competitive and efficient operations.

Solution Delivery & Operations
-Design, build, and maintain automation workflows to streamline IT and business processes.
-Develop, test, and deploy data pipelines for ETL (Extract, Transform, Load) operations.
-Integrate automation and data solutions with existing systems and applications.
-Design, deploy, and maintain MCP (Model Context Protocol) servers to extend AI capabilities and integrate Claude-powered tooling into business workflows.
-Leverage Claude AI and large language model capabilities to automate analysis, generate insights, and accelerate solution development.
-Monitor and optimize the performance of automation scripts and data flows.
-Ensure high availability and reliability of automation jobs and data pipelines.
-Maintain process documentation and contribute to knowledge base articles.
-Ensure compliance with security, regulatory, and industry best practices.

Team Leadership & Development
-Collaborate with cross-functional teams, providing technical guidance and mentorship.
-Share knowledge and best practices with tea ---------- mbers.
-Support professional development and training initiatives related to automation and data engineering, and AI integration practices.

Stakeholder Relationship Management
-Work closely with business stakeholders, data analysts, and IT teams to gather requirements and deliver solutions.
-Communicate effectively with technical and non-technical stakeholders.
-Support project planning, status reporting, and solution presentations.

Process & Continuous Improvement
-Identify and implement improvements to operational workflows, automation frameworks, and data engineering practices.
-Leverage automation, documentation, and tools to increase efficiency and reduce manual effort.
-Track work, time entry, and project progress accurately and promptly.
-Stay current with emerging technologies in automation, data engineering, analytics, and AI/LLM tooling.


JOB REQUIREMENTS:

Knowledge, Skills, and Abilities
-Proficiency in scripting languages (e.g., Python, PowerShell, Bash) and automation platforms (e.g., Power BI, Power Automate).
-Experience with data engineering tools and frameworks (e.g., SQL, ETL tools, Azure Data Factory).
-Experience with Azure Copilot Studio and GitHub Copilot; ability to configure, customize, and govern AI Copilot experiences across the Microsoft 365 ecosystem.
-Hands-on experience designing and configuring MCP (Model Context Protocol) servers, including tool definitions, authentication, and integration with AI assistants such as Claude.
-Strong understanding of database systems (SQL/NoSQL), data modeling, and data integration.
-Familiarity with cloud platforms (e.g., Azure, AWS, GCP) and their automation/data services.
-Strong analytical, problem-solving, and troubleshooting skills.
-Ability to document technical solutions and communicate effectively.
-Strong organizational and time management skills.
-Ability to multitask, adapt to changes quickly, and work effectively in a team environment.

Performance Metrics
-Uptime and reliability of automation jobs and data pipelines.
-Timeliness and quality of automation and data engineering deliverables.
-SLA and KPI adherence for automation and data services.
-Client and stakeholder satisfaction and feedback.
-Contribution to process improvements and team knowledge sharing.
-Adoption and effectiveness of AI/MCP integrations deployed for internal or client use.