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About This Role
Job Description: About the Role
As a Senior AI Engineer I embedded within Secure Power, you leverage generative AI, automation platforms, and low-code tooling to solve real operational problems and remove manual work from the team’s day-to-day. You build, test, and deploy AI agents, automated workflows, and dashboards while maintaining the integrations and infrastructure that keep them running. Your core responsibilities include translating business rules and decision logic into reliable agent instructions, converting manual processes into automated workflows, and building the dashboards leadership relies on to track performance. You automate processes for Secure Power to enhance efficiency and collaborate with process owners and team leaders to develop working prototypes and production-ready tools.
Your mission is to apply specialized technical expertise in AI and automation to identify inefficiencies, map processes, and build new tools that address Secure Power’s most persistent operational pain points. Your work directly impacts how Secure Power runs day to day – contributing to faster cycle times, cleaner data, and better visibility for leadership. You are a specialized technical expert in AI engineering, focused on the delivery, implementation, and design of AI agents, automation, and reporting tools embedded within a single business team. You apply broad technical knowledge to deliver results with direct, visible impact on Secure Power’s operations. You make decisions regarding your own work and priorities, often guided by established precedents, technical standards, and some interpretation. You are responsible for guiding other professionals and providing mentorship on non-routine or escalated issues, and your work is reviewed for accuracy and quality to ensure the highest standards of AI engineering are met.
What You Will Do
- Build and deploy AI agents (e.g., using Microsoft Copilot Studio) that solve real Secure Power business problems
- Design and maintain the automation and dashboard infrastructure (Power Automate, Power Apps, Power BI, Smartsheet) that supports scalable, production-grade solutions for the team.
- Collaborate with process owners and team leaders to map current-state workflows, redesign broken or redundant steps, and transform them into working prototypes and Minimum Viable Products (MVPs).
- Automate workflows and processes across systems the team already uses – including Salesforce, Oracle, CPQ, and SharePoint – to boost efficiency and remove manual re-entry.
- Run pilots, gather feedback, and iterate quickly; create simple documentation so the team can use and own what you’ve built.
- Collaborate with the central AI team and ensure all AI solutions adhere to established governance, security, and responsible AI policies.
(External) English Qualifications: Required Skills
- AI Agent Development: Building, training, and deploying AI agents (e.g., Copilot Studio) into real production use.
- Workflow Automation: Implementing and deploying automated workflows in low-code platforms (e.g., Power Automate, Power Apps).
- Process Mapping & Redesign: Mapping current-state workflows end-to-end and identifying where automation will – and won’t – add value.
- Data Integration: Connecting AI and automation tools across enterprise systems via API (e.g., Salesforce, Oracle) to eliminate manual data re-entry.
- Prompt & Logic Design: Translating business rules and decision logic into clear, testable agent instructions.
- Responsible AI & Governance: Applying data privacy, security, and responsible AI practices, including human review where outputs affect a customer or an order.
Qualifications for Success
- Strong problem-solving mindset with the ability to handle ambiguous, undocumented processes independently.
- Excellent collaboration skills with cross-functional teams to translate business requirements into technical solutions.
- Growth-oriented mindset with strong curiosity to explore new AI and automation technologies and methodologies.
- Adaptability to changing priorities and the ability to navigate a fast-moving, embedded team environment with ease.
- Comfortable explaining a technical tradeoff to a non-technical stakeholder or leader.
Benefits & Growth
- Work on AI and automation projects that directly impact how an entire business team operates.
- Collaborate with Secure Power leaders, process owners, and the central AI team in a supportive, innovation-driven culture.
- Access continuous learning opportunities, certifications, and technical mentorship
Local Benefits (English):
At Schneider, we believe that every employee is a talent who deserves equal opportunities. This means you matter. Every individual needs to feel valued, supported, and treated fairly to do their best work.
Our Total Rewards is our way of saying: “We see you. We value you”. It’s more than just pay and benefits- it’s a meaningful investment in you. It is designed for you to perform, grow, feel safe, and elevate your potential to shine as an impact maker.
For this U.S. based position, the expected pay range is USD 101,600 - USD 152,400 per year. This pay range includes base pay and short-term incentives. The compensation range for this full-time position applies to candidates located within the United States. Our pay ranges are determined by reviewing roles of similar responsibility and level. Within the pay range, individual pay is determined by several factors including performance, knowledge, job-related skills, experience, and relevant education or training.
Schneider Electric is there when it matters most to you
Our Total Rewards package outlines all the benefits and support you’ll enjoy as part of the Schneider Electric team:
Care for Yourself and Your Family. We ensure you feel secure with benefits that help you and your family thrive: medical (with member reward points), dental, vision, and basic life insurance, Benefit Bucks, flexible work arrangements, paid family leaves, well-being programs, 12 holidays per year, and 15 days of paid time off per year.
Invest and Plan Your Future. We help you plan and invest for the future with competitive pay and programs including base salary, incentives, company share ownership, and 401(k) with match.
Grow Your Skills and Career. We support development through performance discussions, global opportunities, the Schneider Career Hub, and learning platforms like Coursera.
Team Up in the Workplace. We encourage collaboration, recognition, sharing your voice, and an inclusive workplace.
Support Your Community. We make a difference through volunteer leave, programs with the Schneider Electric Foundation, youth education initiatives, and military leave benefits.
If you believe this job posting is not compliant with applicable state pay transparency laws in the U.S., please notify the Company as soon as possible upon discovery by completing this form Job Posting Compliance Form*.*
(External) English Company Boiler Plate: Looking to make an IMPACT with your career?
When you are thinking about joining a new team, culture matters. At Schneider Electric, our values and behaviors are the foundation for creating a great culture to support business success. We believe that our IMPACT values – Inclusion, Mastery, Purpose, Action, Curiosity, Teamwork – starts with us.
IMPACT is also your invitation to join Schneider Electric where you can contribute to turning sustainability ambition into actions, no matter what role you play. It is a call to connect your career with the ambition of achieving a more resilient, efficient, and sustainable world.
We are looking for IMPACT Makers; exceptional people who turn sustainability ambitions into actions at the intersection of automation, electrification, and digitization. We celebrate IMPACT Makers and believe everyone has the potential to be one.
Become an IMPACT Maker with Schneider Electric – apply today!
€40 billion global revenue
+9% organic growth
150 000+ employees in 100+ countries
You must submit an online application to be considered for any position with us. This position will be posted until filled. Schneider Electric aspires to be the most inclusive and caring company in the world, by providing equitable opportunities to everyone, everywhere, and ensuring all employees feel uniquely valued and safe to contribute their best. We mirror the diversity of the communities in which we operate, and ‘inclusion’ is one of our core values. We believe our differences make us stronger as a company and as individuals and we are committed to championing inclusivity in everything we do.
At Schneider Electric, we uphold the highest standards of ethics and compliance, and we believe that trust is a foundational value. Our Trust Charter is our Code of Conduct and demonstrates our commitment to ethics, safety, sustainability, quality and cybersecurity, underpinning every aspect of our business and our willingness to behave and respond respectfully and in good faith to all our stakeholders. You can find out more about our Trust Charter here
Schneider Electric is an Equal Opportunity Employer. It is our policy to provide equal employment and advancement opportunities in the areas of recruiting, hiring, training, transferring, and promoting all qualified individuals regardless of race, religion, color, gender, disability, national origin, ancestry, age, military status, sexual orientation, marital status, or any other legally protected characteristic or conduct.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Schneider Electric, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Schneider Electric AI Hiring
Schneider Electric has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Nashville, TN, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
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