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About This Role
Description
Location: Remote, USA
How You'll Contribute to Our Mission
At Frontline Education, our mission is to transform how schools work, so every educator and student succeeds. Our vision is every school thriving. Every community stronger.
As a Solution Architect, Data Platform \& AI within Platform Engineering, you will help define and evolve the architecture behind Frontline's enterprise Data Platform, AI capabilities, and reusable platform services.
Reporting to the Senior Director, Engineering for Platform, you will partner across Product Management, Engineering, Data \& Analytics, Security, and Architecture to turn complex business and technical challenges into scalable, secure, observable, and reusable platform capabilities.
Your influence will extend well beyond individual solutions. You will help establish durable business models, reference architectures, engineering standards, and reusable patterns that enable teams across Frontline to move faster while strengthening the foundation for data\-driven products, analytics, and AI\-enabled experiences.
Success in this role is measured not only by the quality of the architecture you design, but also by whether teams can adopt it successfully and whether the resulting capabilities improve reliability, engineering productivity, customer outcomes, and long\-term platform value.
How You'll Drive Success
Shape Frontline's Data Platform architecture
- Define and evolve the architecture for Frontline's enterprise Data Platform, supporting analytics, integrations, operational workloads, data products, and AI\-enabled experiences.
- Establish reusable patterns for data ingestion, transformation, storage, publication, event streaming, and data exchange across products.
- Define canonical business models, data contracts, versioning approaches, domain boundaries, and shared business concepts that improve consistency across Frontline's portfolio.
- Establish approaches for metadata, lineage, cataloging, discoverability, governance, data quality, observability, and secure data access.
- Enable reusable data products that can be consumed through APIs, events, analytical interfaces, and AI services.
Enable responsible, scalable AI
- Architect reusable capabilities that allow product and engineering teams to adopt AI safely, responsibly, and efficiently.
- Shape architecture for AI\-ready data, retrieval and semantic search, agentic workflows, model and tool integrations, context and knowledge systems, AI evaluation, observability, governance, security, and human oversight.
- Establish reusable integration and governance patterns that reduce isolated implementations and make trusted AI capabilities easier for teams to adopt.
- Evaluate emerging AI technologies thoughtfully, balancing innovation with security, explainability, maintainability, operational maturity, and customer trust.
- Use AI\-assisted tools to accelerate architectural analysis, documentation, design exploration, research, and solution evaluation while applying strong judgment to validate outputs and protect sensitive information.
Build the platform as a product
- Partner with Platform Product Management during discovery and strategic planning to identify reusable capabilities that solve meaningful problems across engineering teams.
- Help translate business and customer needs into scalable technical approaches and long\-term platform investments.
- Inform platform roadmaps, architectural priorities, and build\-versus\-buy decisions using engineering impact, adoption, usability, cost, risk, and business value.
- Design platform capabilities that are intuitive, discoverable, well documented, and easy for engineering teams to adopt.
- Advance self\-service approaches for data publishing, events, APIs, platform capabilities, and developer tooling.
- Use adoption, usability, engineering productivity, reliability, and operational outcomes to evaluate whether platform investments are creating value.
Lead architecture through influence
- Create and maintain reference architectures, Architecture Decision Records, Design Sketches, technical standards, engineering guidelines, reference implementations, and reusable architectural patterns.
- Participate in Architecture Review Board activities and help teams make thoughtful architectural decisions without creating unnecessary gates to delivery.
- Provide architectural guidance and mentorship to Technical Leads, engineers, and cross\-functional partners.
- Communicate complex technical concepts and tradeoffs clearly to technical and non\-technical audiences.
- Bring teams together around shared architectural direction, inviting different perspectives and creating clarity when problems cross organizational boundaries.
- Take ownership of architectural risks, gaps, dependencies, and opportunities, and drive them toward resolution.
Partner from discovery through operations
- Work closely with Product Management, UX, Engineering, QA, Security, DevOps, Data \& Analytics, and Architecture throughout discovery, design, refinement, planning, delivery, and production readiness.
- Help teams make pragmatic tradeoffs between immediate business priorities and long\-term platform sustainability.
- Design for reliability, scalability, security, performance, cost optimization, disaster recovery, multi\-tenant SaaS operations, observability, telemetry, data freshness, data quality, and Service Level Objectives.
- Champion operational excellence as part of architecture from the beginning, not as an afterthought.
- Learn what matters most to the teams and customers affected by platform decisions, and solve for the outcomes they need rather than simply implementing individual requests.
What You Bring to Help Us Grow
- Bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
- 10\+ years of software engineering experience, including 5\+ years designing distributed systems and solution architectures.
- Experience designing cloud\-native, multi\-tenant SaaS platforms and building or evolving enterprise Data Platforms.
- Strong understanding of distributed systems, event\-driven architectures, streaming, asynchronous workflows, APIs, integrations, and data contracts.
- Experience defining data models, canonical business concepts, governance approaches, metadata, lineage, observability, and secure data access patterns.
- Strong experience with AWS cloud services and Infrastructure as Code.
- Experience with Kafka or equivalent streaming and messaging technologies.
- Experience with modern data platforms such as Snowflake, Databricks, Amazon Redshift, Apache Iceberg, Delta Lake, or comparable technologies.
- Experience with modern data engineering and observability technologies such as dbt, Airflow, OpenSearch, DataHub, OpenMetadata, OpenLineage, or OpenTelemetry.
- Experience with REST APIs, GraphQL, API design, and integration architecture.
- Experience collaborating with Product Management during discovery, solution design, and roadmap development.
- Strong understanding of software architecture principles, security architecture, engineering best practices, and operational excellence.
- Excellent written and verbal communication, facilitation, mentoring, and technical leadership skills.
- Experience influencing technical direction across teams and organizational boundaries without relying on direct authority.
Experience in one or more of the following areas will help you make an even greater impact:
- Retrieval\-Augmented Generation, vector search, semantic search, or knowledge retrieval.
- Agentic AI systems, agent orchestration, and tool integration.
- Model Context Protocol and emerging AI integration patterns.
- Knowledge graphs and context management systems.
- AI evaluation, observability, governance, and security.
- Domain\-Driven Design, Event Sourcing, or CQRS.
- Enterprise architecture practices.
- Platform engineering organizations and developer enablement.
- Responsible use of AI\-assisted or agentic development workflows in professional engineering environments.
What You'll Need to Thrive
- You take ownership of outcomes, follow through on commitments, and proactively address architectural risks and opportunities.
- You think in systems, connecting data, platforms, products, architecture, engineering teams, and customer experiences rather than optimizing one component in isolation.
- You balance technical excellence and long\-term sustainability with pragmatic delivery.
- You build trust through collaboration, invite different perspectives, share what you know, and help teams arrive at stronger solutions together.
- You seek to understand what customers and adopting teams are ultimately trying to accomplish and use that understanding to guide technical decisions.
- You are comfortable challenging assumptions, learning from evidence, and improving an approach when a better path becomes clear.
- You communicate complex ideas with clarity and adapt your approach for technical, product, business, and executive audiences.
- You are curious about emerging technologies and actively experiment with new tools and approaches while applying thoughtful judgment.
- You use AI as a practical partner for analysis, exploration, documentation, and problem\-solving, while validating outputs and maintaining appropriate security, privacy, and governance.
- You are comfortable navigating ambiguity and helping others create clarity.
- You enjoy mentoring engineers, strengthening technical practices, and helping organizations scale.
- You measure architecture by what it enables: stronger teams, more reliable platforms, faster delivery, trusted customer experiences, and better outcomes.
Our Mission, Our People, Our Purpose
At Frontline Education, we're reimagining what's possible by becoming an AI\-first organization, transforming how we think, work, and serve the educators who shape our schools every day. By using AI in thoughtful, practical ways, we're creating tools that help educators save time, gain insights, and focus more on what matters most, their students.
As part of our team, you'll be expected and empowered to build and apply AI skillsets that grow with you, because at Frontline Education, technology amplifies what matters most: the human drive to learn, improve, and make a difference.
Compensation \& Benefits
The full base compensation range for this position is $180,000\-$200,000\.
- Bonus eligibility and long\-term incentive opportunities
- 401(k) with company match
- Comprehensive health, dental, and vision coverage
- Employee stock purchase plan
- Generous paid time off and tuition reimbursement
Inclusion, Belonging \& Equal Opportunity
Frontline Education is an equal opportunity/affirmative action employer. We aspire to have an inclusive workplace and strongly encourage suitably qualified applicants from a wide range of backgrounds to apply and join our team.
Interview Process \& Data Privacy
As part of our interview process, Frontline uses video conferencing tools that include photo capture and may include automated transcription features. A screenshot or photo will be taken at the start of the interview for internal identification and record\-keeping purposes only, and transcription may be used to support notetaking and evaluation consistency. These materials are used solely by our recruiting and hiring teams, stored securely, and not shared outside the hiring process. Candidates may opt out of the transcription at any time by notifying their recruiter in advance. Frontline processes this information in accordance with applicable data privacy laws and only for legitimate business purposes related to recruitment and hiring.
Salary Context
This $180K-$200K 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 Frontline Education, 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 ($190K) sits 20% below the category median. Disclosed range: $180K to $200K.
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.
Frontline Education AI Hiring
Frontline Education has 1 open AI role right now. They're hiring across AI Architect. Based in US. Compensation range: $200K - $200K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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