Interested in this AI/ML Engineer role at Legrand?
Apply Now →Skills & Technologies
About This Role
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.
About Legrand
Legrand is the global specialist in electrical and digital building infrastructures. Our comprehensive offering of solutions for residential, commercial, and data center markets makes us a benchmark for customers worldwide. We harness technological and societal trends with lasting impacts on buildings with the purpose of improving life by transforming the spaces where people live, work, and meet with electrical and digital infrastructures and connected solutions that are simple, innovative, and sustainable. Legrand is a global, publicly traded company listed on the Euronext (Legrand SA EPA: LR). For more information, visit www.legrandgroup.com/en
About Legrand North and Central America
Legrand, North \& Central America (LNCA) is a leader in the AV, Lighting \& Controls, Electrical, and Data Center markets. LNCA offers comprehensive medical, dental, and vision coverage, as well as distinctive benefits like a 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, and an active/growing Employee Resource Group network. For more information, visit legrand.us
http://www.legrand.us
http://www.youtube.com/legrandna
http://www.linkedin.com/company/44580
http://twitter.com/legrandNA
Equal Opportunity Employer
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Legrand, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Legrand AI Hiring
Legrand has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.