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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 Engineer 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 Engineer III you will have the opportunity to build, deploy, and support AI\-enabled solutions that improve business processes. This role will work closely with AI architects, data teams, application teams, and business stakeholders to deliver secure, scalable, and production\-ready AI capabilities. The AI Engineer III will help develop machine learning, generative AI, and automation solutions using Microsoft and enterprise technologies while supporting each solution from development through deployment, monitoring, support, and continuous improvement.
So, what will you be doing as an AI Engineer III?
- Design, build, test, deploy, and maintain machine learning, generative AI, and AI\-enabled application solutions for business use cases.
- Develop LLM\-powered applications such as copilots, chatbots, knowledge assistants, summarization tools, agents, and workflow automation solutions.
- Implement retrieval\-augmented generation patterns, including document ingestion, chunking, embeddings, vector search, retrieval logic, response grounding, and evaluation methods.
- Build and maintain data pipelines, APIs, services, connectors, and integrations that embed AI capabilities into enterprise applications and business processes.
- Develop prompt and context engineering approaches, structured outputs, tool or function calling patterns, guardrails, and content filtering for generative AI solutions.
- Use Microsoft and enterprise platforms such as Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Machine Learning, Microsoft Fabric, Power Platform, Microsoft 365, and Dynamics 365 for development, orchestration, deployment, and integration.
- Apply secure development, responsible AI, sensitive data handling, access control, prompt injection protection, and enterprise data governance practices.
- Implement MLOps and LLMOps practices, including version control, automated testing, CI/CD, deployment pipelines, monitoring, rollback processes, and production troubleshooting.
- Collaborate with AI architects, data scientists, data engineers, analysts, DevOps teams, application teams, and business stakeholders to deliver supportable AI solutions.
- Monitor deployed AI services for quality, reliability, usage, cost, performance, errors, drift, security events, business value, and continuous improvement opportunities.
To be successful in this role, you’ll need:
- 5\+ years of experience in software development, data engineering, cloud engineering, or related technical roles.
- 1 to 2 years of hands\-on experience building solutions with generative AI, large language models, copilots, agents, automation, or AI\-enabled applications.
- Working knowledge of retrieval\-augmented generation, embeddings, vector search, prompt and context engineering, structured outputs, and LLM evaluation concepts.
- Experience using cloud\-based AI, machine learning, search, data, or application development platforms to build, integrate, deploy, and support AI\-enabled solutions.
- Proficiency in Python and experience with common AI, data, and application development libraries, frameworks, APIs, and SDKs.
- Experience with Git, automated testing, CI/CD practices, cloud deployment, logging, monitoring, and production troubleshooting.
- Strong understanding of machine learning fundamentals, data structures, model evaluation, software engineering practices, secure development, data privacy, access controls, responsible AI, and enterprise data governance.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Information Technology, or related field.
Things we consider a plus:
- Experience building production AI applications using Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Machine Learning, Microsoft Fabric, Power Platform, Microsoft 365, Copilot Studio, or Dynamics 365 integrations.
- Experience developing APIs, connectors, microservices, or application integrations using Python, C\#, REST APIs, or related technologies.
- Experience with containers, cloud deployment patterns, observability, monitoring, and production incident support.
- Ability to optimize AI solutions for reliability, performance, scalability, cost, maintainability, user adoption, and responsible use.
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
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 Leggett & Platt, 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. 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/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
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