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About This Role
Position Summary
This is a hands\-on role responsible for the Implementation of ServiceNow AI powered capabilities across the ServieNow platform. We are seeking an experienced ServiceNow AI Architect to lead the strategy, architecture, design, and implementation of AI\-powered capabilities across the ServiceNow platform. This role is responsible for delivering enterprise AI solutions utilizing Now Assist, AI Agents, Agentic AI, Generative AI, Predictive Intelligence, Virtual Agent, Intelligent Automation, Document Intelligence, Workflow Data Fabric, RAG (Retrieval\-Augmented Generation), and ServiceNow AI Control Tower while ensuring security, governance, scalability, and measurable business value.
The ideal candidate combines deep ServiceNow architecture expertise with practical experience implementing enterprise AI solutions that improve employee experiences, automate business processes, and reduce operational costs.
Key Responsibilities
AI Strategy \& Architecture
- Define enterprise AI architecture and roadmap for the ServiceNow platform.
- Design scalable AI solutions aligned with organizational business objectives.
- Develop AI governance standards, reference architectures, and implementation best practices.
- Evaluate emerging ServiceNow AI capabilities and recommend adoption strategies.
- Create AI maturity assessments and transformation roadmaps.
AI Solution Design
Architect and implement solutions leveraging:
- Now Assist
- AI Agents (Agentic AI)
- Generative AI
- Predictive Intelligence
- Virtual Agent
- Intelligent Search
- AI Search
- Document Intelligence
- Workflow Data Fabric
- Retrieval\-Augmented Generation (RAG)
- Knowledge Management AI
- AI\-powered Case Summarization
- Incident Summarization
- Change Risk Prediction
- Intelligent Recommendations
- AI Translation
- AI Control Tower
Platform Architecture
- Design enterprise ServiceNow architecture supporting AI workloads.
- Define integration patterns with:
- Microsoft Azure OpenAI
- OpenAI
- Anthropic Claude
- Google Gemini
- Amazon Bedrock
- Enterprise LLM platforms
- Design secure API integrations and AI services.
- Architect scalable vector search and retrieval solutions.
- Ensure high availability and performance optimization.
AI Governance \& Security
- Define AI governance framework.
- Establish responsible AI policies.
- Ensure compliance with:
- GDPR
- HIPAA
- SOC 2
- ISO 27001
- Implement prompt governance.
- Design AI audit and monitoring capabilities.
- Develop AI risk mitigation strategies.
- Ensure secure handling of enterprise data.
Implementation Leadership
- Lead AI implementation projects from discovery through production.
- Mentor developers and technical architects.
- Conduct architecture reviews.
- Establish coding standards.
- Drive Agile delivery.
- Collaborate with executive leadership and business stakeholders.
ServiceNow Platform Responsibilities
Architect solutions across:
- ITSM
- ITOM
- ITAM
- HRSD
- CSM
- FSM
- SecOps
- IRM/GRC
- SPM
- App Engine
- IntegrationHub
- Automation Engine
- Employee Center
- CMDB
- Service Catalog
AI Use Cases
Lead implementation of:
- AI\-powered Incident Resolution
- Automated Knowledge Generation
- AI\-assisted Change Management
- AI Chatbots
- HR Virtual Assistants
- Customer Service AI
- Intelligent Document Processing
- Automated Ticket Classification
- Predictive Case Routing
- AI\-powered Asset Management
- AI Operations
- Software Asset Intelligence
- Executive AI Dashboards
Required Qualifications
- Bachelor's degree in Computer Science, Information Technology, Engineering, or related field.
- 8\+ years of ServiceNow implementation experience.
- 5\+ years designing enterprise ServiceNow architecture.
- 3\+ years implementing AI or Generative AI solutions.
- Experience delivering enterprise\-scale ServiceNow transformations.
- Strong knowledge of ITIL best practices.
- Experience with enterprise integrations and APIs.
- Experience working in Agile environments.
Required Technical Skills
ServiceNow
- Now Platform Architecture
- Flow Designer
- IntegrationHub
- App Engine Studio
- UI Builder
- CMDB
- Discovery
- Service Mapping
- Performance Analytics
- Workflow Automation
- ACLs
- Script Includes
- Business Rules
- Client Scripts
- REST APIs
- SOAP APIs
- GraphQL (preferred)
AI Technologies
- Generative AI
- Large Language Models (LLMs)
- Retrieval\-Augmented Generation (RAG)
- Prompt Engineering
- AI Agents
- Vector Databases
- Semantic Search
- AI Governance
- Natural Language Processing
- Machine Learning fundamentals
- AI Evaluation Frameworks
Integration Experience
Experience integrating with:
- OpenAI
- Azure OpenAI
- Anthropic Claude
- Google Gemini
- Amazon Bedrock
- Microsoft Copilot
- Microsoft Graph
- Slack
- Microsoft Teams
- SharePoint
- Salesforce
- SAP
- Workday
Programming
- JavaScript
- Glide API
- REST APIs
- JSON
- XML
- Python (preferred)
- SQL
Preferred Certifications
ServiceNow
- ServiceNow Certified System Administrator (CSA)
- Certified Application Developer (CAD)
- Certified Implementation Specialist (multiple)
- Certified Technical Architect (CTA) *(preferred)*
- AI\-related ServiceNow certifications *(when available)*
Cloud \& AI
- Microsoft Azure AI Engineer Associate
- Microsoft Azure OpenAI certifications
- AWS Machine Learning Specialty
- Google Professional Machine Learning Engineer
- OpenAI API experience
Soft Skills
- Executive communication
- Strategic thinking
- Enterprise architecture
- Leadership
- Client consulting
- Stakeholder management
- Problem solving
- Innovation mindset
- Cross\-functional collaboration
- Mentoring and coaching
Pay: $85\.00 \- $105\.00 per hour
Education:
- Bachelor's (Preferred)
Work Location: Remote
Salary Context
This $176K-$218K range is above the median for AI Architect roles in our dataset (median: $197K across 33 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 3,708 AI roles we're tracking, AI Architect positions make up 1% of the market. At Clearpath Development LLC, 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. This role's midpoint ($197K) sits 22% below the category median. Disclosed range: $176K to $218K.
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.
Clearpath Development LLC AI Hiring
Clearpath Development LLC has 1 open AI role right now. They're hiring across AI Architect. Based in Remote, US. Compensation range: $218K - $218K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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