Lead AI Engineer

At a Glance Legrand has an exciting opportunity for a Lead AI Engineer to join our Growing AI Team within Legrand North and Central America . This is a remote position. This is a senior, hands-on leadership role at the intersection of enterprise AI engineering, Azure cloud architecture, and technical team management. Our AI team builds and operates MAIA — Legrand's internal AI assistant platform — along with an expanding portfolio of AI-powered automation and integration solutions across LNCA's subsidiaries. As Lead AI Engineer, you will directly lead our development team, take primary ownership of day-to-day technical execution, and serve as the principal engineer on solution architecture — working in close collaboration with the Director of Generative AI to ensure solutions are secure, scalable, and built to last. This role is the right fit for a technically exceptional engineer who is ready to step into genuine leadership: someone who leads through craft and judgment, grows the people around them, and wants to take on increasing architectural ownership over time. What Will You Do? Solution Architecture & Technical Ownership Collaborate with the Director of Generative AI to design end-to-end AI solution architectures — including agentic workflows, RAG pipelines, LLM integrations, and enterprise system connectors — and own the technical execution of those designs. Independently architect solutions for assigned use cases, presenting designs for review and approval prior to development; progressively take on broader architectural ownership as familiarity with the team's standards and systems deepens. Evaluate Azure platform services and select appropriate hosting, storage, and compute patterns (Web Apps, Function Apps, CosmosDB, Container Registry, AI Search, Key Vault, and others) for each solution's specific requirements. Uphold, refine, and enforce engineering standards across the development team: code structure and quality, security and access control, testing and validation, deployment practices, and technical documentation — building on the foundation established by the Director of Generative AI. Deployment, Operations & Security Own the deployment, configuration, and operational management of AI services in Microsoft Azure, ensuring solutions meet enterprise security requirements including private networking, role-based access control, and secrets management. Proactively monitor deployed services, address performance or reliability issues, and maintain documentation to support long-term maintainability and team knowledge continuity. Partner with IT, infrastructure, and security stakeholders to ensure solutions align with Legrand's enterprise architecture and compliance standards. Development Contribute hands-on development work on priority projects — particularly back-end services, agentic framework implementations, and complex API integrations. Build, review, and maintain Python-based services (FastAPI), automation workflows, and integration pipelines; work across the stack as needed including front-end components (React/Vite/Chakra UI) and Node.js integrations. Conduct substantive code reviews for all team members, providing technical feedback that raises quality and develops the team's engineering practices. Team Leadership & Mentorship Directly lead the AI development team, providing day-to-day technical direction, task prioritization guidance, and active unblocking. Take ownership of the team's agile practices — including sprint planning, standups, and retrospectives — ensuring ceremonies run consistently and the team operates with clarity, shared context, and momentum; may delegate facilitation to senior team members as appropriate. Mentor developers at all levels through pair programming, code review, technical coaching, and structured feedback — with particular focus on growing architectural thinking and independent problem-solving. Serve as the primary technical escalation point for the development team. Cross-Functional Collaboration Serve as the development team's steward of the AI-assisted solution refinement process: ensuring the team correctly applies established refinement workflows, maintaining the quality and completeness of work items in Azure DevOps, and continuously identifying opportunities to further improve and automate the refinement pipeline — in partnership with the Director of Generative AI and AI Program Manager. Support the AI Program Director and Director of Generative AI in assessing technical feasibility, effort estimation, and risk identification for proposed use cases. Communicate technical decisions and architectural tradeoffs clearly to both technical peers and non-technical stakeholders. Required Skills Education: Bachelor's or advanced degree in Computer Science, Software Engineering, or a related technical field. Equivalent professional experience in lieu of formal degree will be considered. Experience: 6+ years of professional software engineering experience, with at least 3 years focused on AI/ML development or enterprise AI system implementation. Skills & Qualifications: Strong proficiency in Python, including back-end service development with FastAPI, REST API design and integration, and automation scripting. Hands-on, production experience deploying and managing cloud-hosted services in Microsoft Azure, including: App Services (Web Apps), Function Apps, CosmosDB, Container Registry, Azure AI Search, Key Vault, and Azure networking and security fundamentals. Demonstrated experience building RAG (Retrieval-Augmented Generation) architectures, including vector indexing, semantic search, and LLM API integration (Azure OpenAI or equivalent). Practical experience with agentic AI frameworks — specifically LangChain and/or LangGraph — including multi-step orchestration, tool use, and stateful agent design. Working knowledge of front-end development using React (Vite-based builds); ability to contribute to and review UI layer work as part of full-stack AI solutions. Proven ability to lead, mentor, and develop a team of engineers — including engineers at varying levels of experience and seniority. Strong systems thinking: ability to reason through architecture trade-offs, failure modes, security implications, and long-term maintainability before writing a line of code. Clear, confident written and verbal communicator; comfortable presenting technical designs and recommendations to both technical and non-technical audiences. Preferred: Experience with MCP (Model Context Protocol) or comparable tool-calling and service integration patterns. Familiarity with Node.js as a back-end integration layer. Experience integrating with enterprise platforms such as SharePoint, Confluence, SAP, or similar systems common in large manufacturing or industrial organizations. Exposure to Azure DevOps, CI/CD pipeline configuration, or infrastructure-as-code practices. Experience working within a large, multi-subsidiary or matrixed enterprise environment.