• Work on the platform with a strong focus on MLOps/LLMOps, backend, and integrations. • Build and maintain automation pipelines for continuous deployment of ML and LLM models. • Monitor model performance, detect drift, and automate reprocessing/retraining. • Develop and integrate APIs for communication between AI models and internal systems using FastAPI. • Work with advanced orchestration in Kubernetes and Airflow to ensure scalability and resilience.

• Monitor and maintain the organization’s infrastructure, including servers, networks, storage systems, and applications. • Perform routine system checks and preventive maintenance to ensure optimal performance and uptime. • Respond to system alerts and incidents, diagnosing and resolving issues promptly to minimize downtime. • Provide technical support to resolve infrastructure-related issues, working closely with other technical teams. • Troubleshoot and resolve hardware, software, and network issues, escalating to higher-level support when necessary. • Maintain detailed documentation of issues, solutions, and processes to improve the team’s knowledge base. • Plan and execute system upgrades, patches, and configuration changes, ensuring minimal disruption to business operations. • Test and validate updates in development environments before deploying them to production. • Ensure that all systems comply with security standards and best practices. • Identify opportunities to automate routine tasks and processes, improving operational efficiency and reducing manual workload. • Implement scripts, automation tools, and AI skills to streamline system management and monitoring. • Continuously evaluate and optimize infrastructure performance, capacity, and resource utilization. • Support the development and execution of disaster recovery plans to ensure business continuity in case of system failures. • Manage backup and restore processes for critical systems and data, ensuring data integrity and availability. • Participate in regular disaster recovery testing and drills. • Plan and execute decommissioning of legacy infrastructure, coordinating Terraform state cleanup and DNS cutover. • Work closely with development, network, and security teams to ensure alignment and effective communication on infrastructure projects. • Provide input on infrastructure design and architecture to support new projects and initiatives. • Communicate effectively with non-technical stakeholders, providing updates on system status and issues.

Role Description 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. - Lead our development team - Take primary ownership of day-to-day technical execution - Serve as the principal engineer on solution architecture - Work 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. Qualifications - Experience in enterprise AI engineering - Proficiency in Azure cloud architecture - Strong technical team management skills Requirements - Hands-on experience with AI technologies - Ability to lead and mentor a technical team - Strong problem-solving skills Benefits - Comprehensive medical, dental, and vision coverage - High employer 401K match - Paid time off (PTO) and holiday pay - Short-term and long-term disability benefit plans - Above-benchmark paid maternity and parental leave - Bonus opportunities in accordance with the Company’s incentive plans - Paid time off to volunteer - Active/growing Employee Resource Group network

• As an AI Engineer (m/f/d) you don’t just write notebooks and prompts — you build production-grade GenAI systems: agents, RAG pipelines, and LLM-driven workflows. • You work in cross-functional, agile teams on client projects and engage with the entire modern GenAI stack. • Together with your team you advise clients on use-case identification, architectural decisions, effort estimation, and the selection of appropriate delivery models. • You analyze client requirements, design and implement agent-based solutions and RAG pipelines over enterprise data, and ensure clear, high-quality documentation. • You leverage modern, automated approaches and AI-native developer workflows to optimize code and deliver innovative solutions.