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About This Role
Date
Jul 13, 2026
Location
Carthage, Missouri, United States
Company
Leggett \& Platt
We, at Leggett \& Platt Inc., are searching for an AI Architect III within our Corporate ITteam to help support our business. As a global\-diversified manufacturing company, it’s sometimes hard to explain all the different things we do. We like to say, “we’re the biggest company no one has ever heard of.” We are confident you interact with one of our products in your daily life without knowing it. Whether it’s the mattress you sleep on, the car you drive, the plane you fly on, or the furniture you sit on, our high\-quality components are there supporting you. If you join our team, your work will ensure people across the world have a little more comfort in their lives.
As an AI Architect III you will have the opportunity to partner with business and technology teams to identify opportunities where AI can improve the way work gets done. Your contributions will have a direct impact on the business by evaluating business needs, recommending practical AI solutions, and guiding the design of secure, scalable, and supportable capabilities. The AI Architect III will also help determine when to build solutions internally and when to use vendor or Microsoft\-based tools, while supporting each solution from early discovery through deployment, ongoing support, and continuous improvement.
So, what will you be doing as an AI Architect III?
- Partner with business teams to understand operational challenges, define requirements, and identify AI opportunities that improve processes, decisions, productivity, or user experience.
- Review proposed AI use cases for business value, feasibility, data readiness, security, privacy, supportability, and alignment with enterprise needs.
- Design end\-to\-end AI solution architectures, including data flow, integrations, access controls, deployment approach, monitoring, and support requirements.
- Define reusable architecture patterns, design templates, standards, and decision criteria to support consistent AI solution delivery across the enterprise.
- Evaluate build, buy, or reuse options using Microsoft technologies, existing enterprise platforms, vendor solutions, internal tools, and emerging AI capabilities.
- Partner with security, infrastructure, data, compliance, application, and support teams to review risks, data sources, integrations, responsible AI controls, cost, scalability, and long\-term maintainability.
- Guide implementation planning, testing, deployment, documentation, user readiness, operational support transition, monitoring, and continuous improvement for AI\-enabled solutions.
- Maintain awareness of emerging AI technologies, platform capabilities, risks, regulations, and industry practices that may impact solution design.
To be successful in this role, you’ll need:
- 5\+ years of experience in IT architecture, solution design, application development, data platforms, automation, cloud platforms, or related technology roles.
- 1 to 2 years of hands\-on or architecture experience with generative AI, large language models, agents, automation platforms, and AI\-enabled business applications.
- Working knowledge of retrieval\-augmented generation, vector search, embeddings, prompt and context engineering, and LLM evaluation concepts.
- Strong understanding of data ecosystems, cloud platforms, APIs, integration patterns, identity and access controls, data classification, security, and responsible AI principles.
- Experience with enterprise architecture, solution architecture, application lifecycle practices, MLOps or LLMOps concepts, and production support models.
- Ability to translate business needs into technical requirements, document solution tradeoffs, and explain AI concepts in clear business terms.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Information Technology, Engineering, Business, or related field.
Things we consider a plus:
- Experience designing or implementing AI solutions with Microsoft technologies such as Azure Machine Learning, Azure OpenAI, Azure AI Foundry, Azure AI Search, Microsoft Fabric, Power Platform, Microsoft 365, or Dynamics 365\.
- Experience establishing AI governance practices, including model lifecycle management, logging, auditability, risk review, and responsible AI controls.
- Experience evaluating vendor AI tools, enterprise platform capabilities, and third\-party solutions against business, technical, security, and cost requirements.
- Ability to balance business value, user adoption, cost, scalability, maintainability, and operational readiness when recommending AI solutions.
- Practical delivery mindset with experience moving AI solutions from discovery and design into implementation planning, support transition, and continuous improvement.
What to Do Next
Now that you’ve had a chance to learn more about us, what are you waiting for! Apply today and allow us the opportunity to learn more about you and the value you can bring to our team. Once you apply, be sure to create a profile, and sign up for job alerts, so you can be the first to know when new opportunities become available.
Our Values
Our values speak to our shared beliefs, and describe how we approach working together.
- Put People First reflects our commitment to safety and care of each other, learning and development, and creating an inclusive environment of mutual respect, empathy and belonging.
- Do the Right Thing focuses us on acting with honesty and integrity, delivering the results the right way, taking pride in our work, and speaking the truth – good or bad.
- Do Great Work…Together occurs when we engage without hierarchy, collaborate as a team, embrace challenges, and work for the good of all of us.
- Take Ownership and Raise the Bar demonstrates our responsibility to add value and make a difference, challenge the status quo and biases to make things better, foster innovative and creative solutions to drive impact, and explore new perspectives and embrace change.
Our Commitment to You
We're actively taking steps to make sure our culture is inclusive and that our processes and practices promote equity for all. Leggett \& Platt is comprised of people of all abilities, gender identities and expressions, ages, ethnicities, sexual orientations, veteran status, and more. Join us!
We welcome and encourage applications if you meet the minimum qualifications. Even if you do not meet the preferred qualifications, we’d love the opportunity to consider you.
Equal Employment Opportunity/Veterans/Disability Employer
For more information about how we handle your personal data in connection with our recruiting processes, please refer to the Recruiting Privacy Notice on the “Privacy Notice” tab located at http://privacy.leggett.com
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,708 AI roles we're tracking, AI Architect positions make up 1% of the market. At Leggett & Platt, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $254,798 based on 67 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Leggett & Platt AI Hiring
Leggett & Platt has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Based in Carthage, MO, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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
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