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About This Role
Position Summary
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We are looking to add to our dynamic team the critical role of Enterprise AI Architect to help turn AI ideas into secure, scalable, production\-ready business solutions. This high\-visibility role will define the architecture, patterns, and guardrails that help the company adopt AI responsibly and at scale.
The ideal candidate is a hands\-on solution architect who can translate business needs into practical AI solutions, design agentic and multi\-agent architectures, and partner across business, IT, data, cybersecurity, and operations teams.
This is a builder role for someone excited to create the enterprise AI playbook in a global manufacturing and technology environment.
Why This Role Is Exciting
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- Help define how enterprise AI is built, governed, and scaled.
- Work on high\-value AI use cases that improve real business processes.
- Shape the company's approach to agents, copilots, AI governance, and responsible adoption.
- Turn experimentation into measurable enterprise impact.
Key Responsibilities
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AI Strategy \& Solution Architecture
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- Define the enterprise AI architecture roadmap, from early use cases to production\-ready solutions.
- Create reusable standards, solution patterns, and best practices for scalable AI delivery.
- Lead architecture for generative AI, copilots, AI agents, RAG, machine learning, and intelligent workflows.
- Design agentic and multi\-agent solutions with clear controls, escalation paths, and human\-in\-the\-loop checkpoints.
Azure AI Platform Leadership
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- Architect solutions using Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Microsoft Fabric, Copilot Studio, Power Platform, and related Microsoft AI services.
- Define when to use copilots, agents, RAG, automation, custom APIs, or third\-party AI tools.
- Evaluate and integrate AI capabilities from outside the Azure ecosystem, including platforms and models from providers such as OpenAI, Anthropic, Google, and others.
- Design hybrid AI patterns for manufacturing and operational environments that cannot be fully cloud\-native.
Enterprise Data \& Systems Integration
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- Ground AI solutions in trusted enterprise data, including ERP, SQL Server applications, and manufacturing/OT systems.
- Define secure data pipelines, APIs, connectors, and integration patterns using standards such as MCP and A2A where appropriate.
Cross\-Functional Collaboration
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- Partner with business leaders, cybersecurity, infrastructure, data, and development teams to deliver secure, scalable AI solutions.
- Prioritize AI opportunities based on business value, feasibility, risk, and adoption potential.
Agile Delivery Leadership
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- Provide technical leadership across Agile delivery teams, including onshore and offshore resources.
- Guide AI initiatives from concept through production deployment and support.
AI Governance, Risk \& Compliance
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- Establish responsible AI, security, compliance, and governance standards for production AI solutions.
- Define ALM, LLMOps/MLOps, monitoring, versioning, telemetry, and model evaluation practices.
- Protect AI models and data workflows through access controls, audit trails, data residency, and prompt\-injection safeguards.
AI Cost Governance (FinOps)
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- Monitor AI compute, API, and cloud costs.
- Conduct ROI analysis and define success metrics for AI\-powered solutions.
Required Qualifications
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Experience
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- 5\+ years in solution, cloud, or enterprise architecture.
- 3\+ years designing AI, machine learning, generative AI, or agentic AI solutions.
- Hands\-on experience with Microsoft Azure and Azure AI services.
- Experience integrating AI with enterprise systems, ERP, manufacturing, or operational data is a plus.
- Experience leading Agile teams and globally distributed development resources.
Technical Skills
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- Azure OpenAI, Azure AI Foundry, Azure Machine Learning, Microsoft Fabric, Copilot Studio, Power Platform.
- LLMs, RAG, AI agents, prompt engineering, grounding, evaluation, telemetry, and human\-in\-the\-loop patterns.
- Ability to compare and select fit\-for\-purpose AI platforms, models, and tools across Microsoft and non\-Microsoft ecosystems.
- MCP, A2A, secure APIs, connectors, cloud architecture, and enterprise integration patterns.
- Security, identity, governance, MLOps/LLMOps, and regulated\-environment awareness.
Preferred Certifications
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- Microsoft Certified: Azure Solutions Architect Expert.
- Microsoft Certified: Azure AI Engineer Associate (or equivalent GenAI/ML certification).
Soft Skills
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- Strong communicator who can explain AI concepts to technical and non\-technical audiences.
- Collaborative partner with strong stakeholder management skills.
- Practical, outcome\-focused problem solver who can balance innovation with governance.
EEO\-M/F/D/V
\#Itasca
Shift: First Shift
Compensation: $170,000 \- $220,000
Additional Details : Knowles is a leading manufacturer of specialty electronic components. We design parts that perform unique, critical functions for innovative technologies. Through extreme reliability, custom engineering, and scalable manufacturing, we enable businesses to succeed in the most demanding applications across medtech, defense, and industrial markets. Our high\-performance capacitors, RF and microwave filters, advanced medtech microphones, balanced armature speakers, and miniaturization products enable and enhance the performance of technologies with the power to change, improve, and save lives. Founded in 1946 and headquartered in Itasca, Illinois, Knowles has grown into a global organization with employees spanning 11 countries.
Salary Context
This $170K-$220K range is above the median for AI Architect roles in our dataset (median: $181K across 29 roles with salary data).
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 4,317 AI roles we're tracking, AI Architect positions make up 1% of the market. At Knowles Corporation, 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 $237,300 based on 102 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($195K) sits 18% below the category median. Disclosed range: $170K to $220K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Knowles Corporation AI Hiring
Knowles Corporation has 1 open AI role right now. They're hiring across AI Architect. Based in Itasca, IL, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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