Role Description We are seeking a highly hands-on AI Engineer who thrives in building real-world, production-ready solutions. This role is focused on designing, developing, and deploying end-to-end AI agent solutions using modern tooling—primarily within the Microsoft ecosystem. You will work in a fast-paced, innovation-driven environment, taking ambiguous business problems and turning them into scalable, pragmatic AI-powered solutions. This is a core engineering role requiring strong ownership, execution speed, and deep hands-on expertise in AI/LLM systems. What You Will Be Doing: - Build and deploy AI agents and multi-agent systems on Azure using composable patterns - Build and consume MCP servers to connect agents to internal data sources, tools, and enterprise workflows - Design orchestration patterns, tool integrations, and RAG pipelines - Develop prompt engineering and evaluation workflows (including automated eval loops) - Build APIs for agent integration with enterprise platforms (M365 Copilot, Teams) - Implement data pipelines for unstructured data (transcripts, support interactions) - Set up CI/CD, responsible AI controls, and production monitoring - Use agentic dev tools (GHC CLI, Claude Code, Cursor, Copilot agent mode) as your primary development workflow - Flex into low-code agent configuration, infrastructure, and data engineering as needed Qualifications - 7+ years of software engineering experience, with at least 3+ years building GenAI/LLM-based solutions in production environments - Strong hands-on experience in AI agent development, including building and deploying agents using platforms such as Azure AI Foundry, Microsoft Agent Framework, or similar - Proven expertise in LLM/GenAI engineering, including prompt engineering, model selection, evaluation, and structured outputs - Advanced proficiency in Python, with experience writing production-quality, scalable, and well-tested code - Experience designing and building RESTful APIs for enterprise-grade applications - Strong understanding of Azure cloud infrastructure, including deployment pipelines, identity, and search services - Experience building RAG pipelines, orchestration patterns, and tool integrations - Hands-on experience working with AI-native developer tools such as GitHub Copilot CLI, Claude Code, Cursor, or similar - Ability to build and manage data pipelines for unstructured data (e.g., transcripts, support interactions) - Experience implementing CI/CD pipelines, monitoring, and responsible AI practices - Demonstrated experience in building real AI systems (not just API integrations) Requirements - Experience with MCP server development or similar agent-tool integration protocols - Exposure to LLM/ML Ops, including evaluation frameworks and automated testing loops - Familiarity with PySpark, NLP pipelines, or large-scale data processing - Experience with Copilot Studio and Power Platform - Knowledge of containerization technologies (Docker, Kubernetes) - Front-end development experience for building AI-integrated user interfaces - Experience integrating AI solutions with enterprise tools such as Microsoft Teams and M365 Copilot - Strong understanding of agent evaluation frameworks and automated evaluation workflows - Full-stack development experience is a plus Benefits - Equal employment opportunities to all employees and applicants for employment - Prohibits discrimination and harassment of any type - Policy applies to all terms and conditions of employment, including recruiting, hiring, and the full employee lifecycle

• Design and implement agentic development workflows • Establish quality engineering practices embedded in the development process • Evaluate AI agent output for correctness, security, and architectural integrity • Build and maintain AI-powered features, integrations, and internal tooling • Define guardrails and risk governance for AI-assisted development • Mentor developers on effective agent-directed workflows, prompt engineering, and critical evaluation of AI output • Make architectural calls that AI agents cannot • Drive test automation strategy and implementation as part of the development lifecycle

• Translate business problems into clear analytical approaches and solution designs • Wrangle datasets and engineer features to support ML modeling and AI workflows • Build, evaluate, and iterate on ML models (forecasting, classification, optimization) • Support the development of LLM-based workflows and emerging agentic AI patterns as these initiatives ramp up • Contribute to deploying, monitoring, and maintaining AI and ML workflows in production, working closely with senior engineers • Stay curious about new AI development tools and frameworks, and bring them into our workflows where they fit

• Owning and defining the AI curriculum roadmap, including what is taught, what is prioritized, and what is removed as the landscape evolves • Conducting ongoing research and validation of emerging AI tools, models, and workflows to determine practical applications for founders and operators • Creating, documenting, and maintaining proprietary methodologies, frameworks, and intellectual property that can be consistently taught and scaled • Establishing and maintaining educational standards across AI-related curriculum and learning experiences • Teaching live and on camera alongside the Master Instructor in front of large student audiences • Translating complex AI concepts into actionable business strategies and implementation frameworks • Supporting the development of AI-powered coaching and advisory experiences to ensure consistency across student interactions • Partnering with internal stakeholders to continuously improve educational outcomes and student success • Staying at the forefront of AI advancements and evaluating their relevance to business owners and entrepreneurs.