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About This Role
Job Description
SUMMARY: The AI Architect is responsible for designing and delivering production\-ready AI solutions across the organization. This individual will manage a small team of AI Engineers and occasionally flex roles from UX, front\-end, and subject matter experts to create innovative solutions that leverage machine learning for enterprise\-wide impact. The AI Architect will provide direct oversight of AI solution design, infrastructure, and deployment using standardized coding best practices. This position will mentor team members in technical development and ensure alignment with business objectives in the K12 and broader enterprise context. The AI Architect is deeply experienced, passionate about applied AI, comfortable building end\-to\-end ML pipelines in cloud environments, and able to set priorities and organize the collaborative work of a small technical team. They will drive the timely delivery of AI products directly linked to actionable business decisions.
ESSENTIAL FUNCTIONS: Reasonable accommodations may be made to enable individuals with disabilities to perform the essential duties.
- Drives the conception, prototyping, and deployment of machine learning models—particularly in natural language processing (NLP) and recommender systems—across multiple product lines;
- Leverages AWS and Azure ecosystems, containerization, and infrastructure\-as\-code tools to create and maintain production\-ready AI systems that can scale efficiently;
- Working from proof\-of\-concept through full productization, including data ingestion, model training, deployment, monitoring for performance drift, and iteration for continuous improvement;
- Informs, influences, and supports product decisions.
- Directly supervises 1–3 AI Engineers and occasional flex contributors on specific projects. Provides guidance on coding best practices, model development, and data governance;
- Works closely with Product, Engineering, IT Managers, Data Stewards, and high\-level executives to align AI initiatives with organizational objectives. Ensures that AI solutions adhere to governance and compliance requirements, including student data privacy;
- Provides critical input and execution support, identifying practical opportunities for applied AI to drive business outcomes;
- Ensures that AI deliverables are well\-documented, reproducible, and monitored for ongoing performance and data integrity. Partners with governance teams and legal to adapt to evolving data privacy regulations and responsible AI practices;
- Stays abreast of emerging trends and tools—such as large language models—and evaluates their applicability. Advocates for robust DevOps/MLOps, agile methodologies, and cross\-functional collaboration to maximize the impact of AI deployments;
- Aligns K12’s AI capabilities with the needs of our schools \& school services teams that will drive the adoption and use of actionable data to deliver improved outcomes;
- Identifies and defines strategic opportunities for leveraging data science across other businesses and functions in support of K12’s mission, vision and long\-term strategy;
- Demonstrates a passion for education and the K12 experience, actively motivating and encouraging the same passion in employees.
Supervisory Responsibilities: *Directly supervises 1 \- 3 Full\-time Equivalent (FTE) regular employees and/or contractors. Carries out supervisory responsibilities in accordance with the organization's policies and applicable laws. Responsibilities include interviewing, hiring, and training employees; planning, assigning, and directing work; appraising performance; rewarding and disciplining employees; addressing complaints and resolving problems.*
MINIMUM REQUIRED QUALIFICATIONS:
- Bachelor’s degree in Computer Science, Math, Physics, Engineering, or related quantitative field AND
- Six (6\) years’ related experience; OR
- Equivalent combination of education and experience
Certificates and Licenses: None required. Certifications such as AWS Solutions Architect or Azure Solutions Architect are a plus but not mandatory.
OTHER REQUIRED QUALIFICATIONS:
- Experience designing and deploying machine learning models—especially NLP and recommender systems—using Python and common ML libraries (e.g., PyTorch, TensorFlow, scikit\-learn).
- Proficiency with AWS, Azure, Docker, Kubernetes, and Terraform to build scalable, secure, and high\-performing environments.
- High attention to detail and high level of accuracy.
- Strong analytical skills.
- Strong customer service orientation.
- Professional integrity necessary to maintain confidentiality.
- Strong planning skills and ability to manage multiple projects simultaneously.
- Excellent verbal and written communication skills.
- Ability to complete assigned duties within critical deadlines.
- Ability to work independently and in a team environment.
- Proficient with Microsoft Office (Word, Excel, PowerPoint, and Outlook).
- Proficiency with modern reporting platforms and tools.
- Ability to grasp complex platform concepts and business models, in a digital context.
- Ability to clear required background check
PREFERRED QUALIFICATIONS:
- Experience with MLOps frameworks such as MLflow or Kubeflow.
- Familiarity with K12 education data or prior EdTech experience.
- Experience working in an environment that emphasizes responsible AI, ethical AI, or complex governance practices.
WORK ENVIRONMENT: The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- This is a home\-based position
Compensation \& Benefits: Stride, Inc. considers a person’s education, experience, and qualifications, as well as the position’s work location, expected quality and quantity of work, required travel (if any), external market and internal value when determining a new employee’s salary level. Salaries will differ based on these factors, the position’s level and expected contribution, and the employee’s benefits elections. Offers will typically be in the bottom half of the range.
We anticipate the salary range to be $113,073\.75 to $180,000\.00\. Eligible employees may receive a bonus. This salary is not guaranteed, as an individual’s compensation can vary based on several factors. These factors include, but are not limited to, geographic location, experience, training, education, and local market conditions. Stride offers a robust benefits package for eligible employees that can include health benefits, retirement contributions, and paid time off.
Job Type
RegularThe above job is not intended to be an all\-inclusive list of duties and standards of the position. Incumbents will follow any other instructions, and perform any other related duties, as assigned by their supervisor. All employment is “at\-will” as governed by the law of the state where the employee works. It is further understood that the “at\-will” nature of employment is one aspect of employment that cannot be changed except in writing and signed by an authorized officer.
*If you are a job seeker with a disability and require a reasonable accommodation to apply for one of our jobs, you can request the appropriate accommodation by contacting* *stridecareers@k12\.com.*
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
Stride, Inc. is an equal opportunity employer. Applicants receive consideration for employment based on merit without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status, or any other basis prohibited by federal, state, or local law. Stride, Inc. complies with all legally required affirmative action obligations. Applicants will not be discriminated against because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant.
Salary Context
This $113K-$180K range is below the median for AI Architect roles in our dataset (median: $159K across 26 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 3,963 AI roles we're tracking, AI Architect positions make up 1% of the market. At Stride, Inc., 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 $212,500 based on 100 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400. This role's midpoint ($146K) sits 31% below the category median. Disclosed range: $113K to $180K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Stride, Inc. AI Hiring
Stride, Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Based in Remote, US. Compensation range: $145K - $180K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,883 positions. About 15% 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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), AI Software Engineer (279). 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $290,000 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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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