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About This Role
AI Architect
*Manufacturing Operations \| Enterprise AI \& Automation \| Orlando, Florida \| On\-Site*
We are seeking an experienced AI Architect to design, govern, and scale our enterprise AI ecosystem. This is a senior, hands\-on role for a builder\-leader who can define the AI operating model, architect the platform, ship production\-grade agents, and drive measurable business outcomes across a multi\-plant manufacturing environment.
About the Role
The AI Architect owns how AI is designed, deployed, governed, and scaled across the company. The position spans strategy and execution — setting standards and architecture on one hand, and personally building agents, integrations, and automations on the other. The role partners closely with leadership and functional teams across manufacturing, supply chain, quality, maintenance, finance, sales, customer service, and corporate operations.
*This is not a project\-implementer role. It is the AI operating\-model owner for the enterprise.*
Key Responsibilities
AI Strategy \& Operating Model Ownership
- Own the enterprise AI operating model, roadmap, and architecture — setting standards for design, security, compliance, and lifecycle management.
- Serve as the primary advisor to leadership on AI capabilities, risks, ROI, and platform investment decisions.
Agent Development \& AI Solutions
- Design, build, and deploy production AI agents, copilots, and intelligent workflows across operations, finance, supply chain, and commercial functions.
- Engineer prompt frameworks, orchestration logic, multi\-agent handoffs, and a reusable component library that accelerates future deployments.
Enterprise Microsoft AI Platform Leadership
- Architect and administer solutions across Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Power Platform, Fabric, and Dataverse.
- Define tenant\-level standards for agent deployment, identity, permissions, DLP, data residency, and governance.
Integration, APIs \& Interoperability
- Design and implement API\-driven integrations between AI solutions and ERP, HRIS, quality, maintenance, and other core business systems.
- Apply modern interoperability patterns — REST, GraphQL, event\-driven, MCP, and RAG — to connect AI securely to enterprise data.
AI Governance, Evaluation \& Production Monitoring
- Establish governance for AI agents, models, and automations — covering intake, approval, versioning, evaluation, and decommissioning.
- Monitor production AI for quality, drift, cost, and business outcomes; implement auditability and responsible\-AI controls.
Operational Improvement \& Change Leadership
- Partner with plant, functional, and executive leaders to identify and prioritize high\-value AI opportunities.
- Drive cross\-functional adoption tied to measurable outcomes — productivity, quality, throughput, and decision\-making.
Qualifications
Required
- Demonstrated experience designing, building, and operating AI agents, copilots, or generative AI applications in production — not just chatbots, pilots or demos.
- Deep, hands\-on expertise with the Microsoft enterprise stack: Microsoft 365 Copilot, Copilot Studio, Power Platform, Azure AI services, Fabric, Dataverse, SharePoint, and Teams.
- Meaningful working experience with at least one non\-Microsoft AI platform (e.g., OpenAI, Anthropic, Google, AWS, or open\-source LLM ecosystems).
- Strong experience with API integrations, custom connectors, authentication, and data pipelines across enterprise systems.
- Practical understanding of AI governance, evaluation, security, and production monitoring.
- Ability to communicate clearly with executives, technical teams, and front\-line employees, and to translate business problems into AI solutions.
Preferred
- Experience with agent frameworks and interoperability standards (e.g., MCP, LangChain, Semantic Kernel, AutoGen).
- Experience with retrieval systems, vector databases, and enterprise knowledge grounding.
- Experience integrating AI with ERP, or shop\-floor systems in a manufacturing or supply\-chain environment.
- Experience defining enterprise AI architectures spanning multiple vendors, models, and data platforms.
- Manufacturing, distribution, or multi\-site operations experience.
Location \& Work Arrangement
Location: Orlando, Florida. This is an on\-site role in a manufacturing environment.
Compensation \& Benefits
Salary range: commensurate with experience and the scope of enterprise ownership.
Benefits may include:
- Medical, dental, vision, and life insurance options
- Paid time off and paid holidays
- 401(k) retirement plan
How to Apply
Submit your resume along with a brief description of an AI agent or solution you personally designed and shipped to production — including the problem, architecture, models and platforms used, integrations, governance approach, and measurable business impact.
Equal Opportunity Employer
We are an Equal Opportunity Employer and consider all qualified applicants for employment without regard to race, color, religion, sex, national origin, disability, veteran status, age, genetic information, or any other status protected by applicable law.
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 Sherwood Bedding, 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.
Sherwood Bedding AI Hiring
Sherwood Bedding has 1 open AI role right now. They're hiring across AI Architect. Based in Orlando, FL, 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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