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About This Role
Job Overview
The AI Architect will design and implement end\-to\-end architectures for Generative AI and AI/ML solutions, working under the guidance of the Director Enterprise Systems to bring the organization’s AI strategy to life. Our EdTech products and services touch millions of students and educators every year so our AI solutions must be guided by our purpose to help every teacher and student feel seen, valued and supported. This role is deeply technical, hands\-on, and delivery\-focused. The ideal candidate has strong experience with data\-driven AI, GenAI architectures (especially RAG), cloud\-native patterns, and MLOps/LMMOps tooling. They will be a key contributor to the build‑out of enterprise AI platforms and accelerators. The AI Architect translates architectural standards into real solutions, builds reusable components, partners closely with data engineering and product teams, and ensures AI projects are secure, scalable, compliant, and production‑ready.
Job Responsibilities:
1\. Solution Architecture \& Delivery* Design secure end\-to\-end AI solution architectures including data ingestion, model training, inference pipelines, orchestration flows, and integration with downstream systems.
- Implement architectures defined by the Director Enterprise Systems, ensuring alignment with standards, patterns, and platform strategy.
- Build GenAI solutions using RAG, vector search, grounding strategies, prompt orchestration, and model evaluation frameworks.
- Create and maintain high\-quality HLD/LLD documentation, sequence diagrams, and data flows for AI workloads.
- Perform hands\-on technical proofs of concept, evaluate models/tools, and convert prototypes into production\-grade systems.
2\. AI Engineering Enablement* Build secure reusable code templates, libraries, and patterns for deployment, evaluation, and monitoring of workloads.
- Partner with engineers to integrate AI components into pipelines, data products, and operational workflows.
- Implement model lifecycle management: versioning, experimentation tracking, registry integration, automated deployment.
3\. Data \& Retrieval Architecture* Architect federated data access patterns for AI, integrating multiple source systems (data lakes, warehouses, content repositories, SaaS platforms) into cohesive retrieval pipelines.
- Design data pipelines that support AI use cases: feature engineering, embedding generation, chunking strategies, and retrieval flows.
- Implement and optimize vector DB schemas, embeddings, and hybrid search (keyword \+ semantic) patterns.
- Ensure data quality, lineage, access controls, and privacy protections align with enterprise requirements.
4\. Governance, Security \& Compliance* Partner with Security and Privacy teams to ensure AI solutions align with applicable regulations and standards, translating policy requirements into enforceable technical controls.
- Apply responsible AI principles, ensuring solutions include safety, bias mitigation, grounding, and hallucination safeguards.
- Establish guardrails for model training and prompt usage, including restrictions on sensitive data ingestion and prevention of model leakage.
- Implement enterprise\-approved security patterns (private endpoints, tokenization/MPC, encryption, IAM roles, network controls).
- Conduct architecture reviews, risk assessments, and model evaluations as part of deployment readiness.
5\. Collaboration \& Communication* Collaborate with product managers, engineers, and business stakeholders to refine requirements and translate them into technical specifications.
- Provide clear technical guidance, mentoring, and code reviews for teams using AI services.
- Communicate trade\-offs, limitations, and risks of different AI approaches to both technical and non\-technical audiences.
- Maintain a strong business perspective to ensure systems are implemented in ways that support operational goals and user needs.
- Ensure solutions meet high standards of quality, with successful delivery driven by thorough testing and validation practices.
- Provide technical guidance to other engineering team members, fostering growth and knowledge sharing.
Job Requirements:* 5\+ years of experience in engineering and architecture roles, operating at enterprise scale and demonstrating sustained ownership of complex data and AI enabled platforms.
- Hands\-on experience designing and implementing:
+ LLM\-based solutions (RAG, tool use, prompt workflows, fine\-tuning where appropriate).
+ Traditional ML models and pipelines.
+ Cloud AI services (Azure OpenAI/AI Studio, AWS Bedrock/SageMaker, GCP Vertex AI).
+ Vector databases \& search (Azure AI Search, Pinecone, Weaviate, OpenSearch, pgvector).
+ Data pipelines supporting AI (Spark, Databricks, Synapse, Snowflake, dbt, Airflow).
- Strong understanding of MLOps/LMMOps practices including model registry, CI/CD for ML, monitoring, and evaluation.
- Proficiency in Python and familiarity with key AI frameworks (LangChain/LangGraph, Semantic Kernel, PyTorch or TensorFlow, MLflow).
- Working knowledge of security, governance, and compliance controls related to AI.
Preferred Qualifications* Experience operationalizing agentic workflows, copilots, or AI\-enabled automation within enterprise environments.
- Hands\-on experience with model evaluation frameworks (quality, bias, safety, robustness, hallucination tests).
- Experience implementing observability for AI: telemetry, token usage, latency, grounding metrics.
- Familiarity with event\-driven and microservices architectures.
- Certifications such as:
+ Azure AI Engineer Associate
+ Azure Solutions Architect Associate
+ AWS Machine Learning Specialty
+ GCP Professional ML Engineer
Behavioral Competencies* Strong problem\-solving abilities with a practical, delivery\-first mindset.
- Curiosity and willingness to explore and quickly learn emerging AI technologies.
- Ability to work collaboratively with engineering teams while following architectural guidance.
- Clear communicator with the ability to simplify complex topics.
Measures of Success* Architecture deliverables aligned with standards set by the Senior Solutions Architect.
- Reduction in engineering effort through reusable components and accelerators.
- Successful deployment of AI workloads meeting performance, security, and cost criteria.
- Improved reliability and monitoring coverage for AI solutions.
- Demonstrated ability to operationalize GenAI solutions in production environments.
Example Day\-to\-Day Work* Build a RAG pipeline for a business unit using enterprise vector search, integrating with the central AI platform.
- Assist engineering teams adopting new AI building blocks (function calling, prompt orchestration, hybrid search).
- Run model evaluations to compare open‑weight vs. hosted models for a specific workload.
- Draft LLD diagrams for an AI\-powered automation feature and review with the Senior Solutions Architect.
- Implement observability dashboards tracking grounding quality, latency, and cost per inference.
To learn more about our organization and the exciting work we do, visit www.cambiumlearning.com
Remote First Work Environment
Our Remote First approach gives employees the flexibility and trust they need to effectively balance work with life. It creates a culture in which all employees are valued and where success is measured in results. It allows us to work collaboratively, inclusively and for greater positive impact, regardless of our individual locations.
If you will be working remotely, either occasionally or on a permanent basis, you must have a reliable internet connection through a cable or fiber\-optic broadband service with minimum speeds of 10 Mbps download and 5 Mbps upload.
The successful candidate will be expected to actively participate in video\-based interviews during the recruiting process and ongoing virtual meetings with their camera on, as part of their role. To maintain confidentiality and ensure a fair evaluation process, the use of note\-taking tools, reference materials, or AI\-powered tools (including generative AI, language models, or similar technologies) during interviews or other selection activities is prohibited unless prior written approval has been obtained from the People Experience team. If you require an exception for medical, accessibility, or other reasons, please contact your Talent Acquisition team member to discuss accommodations in advance.
As part of our Remote\-First benefits, Cambium offers reimbursement to help cover the cost of setting up your home or remote office.
An Equal Opportunity Employer
We are dedicated to fostering a culture that celebrates unique backgrounds, ideas, and experiences. All qualified applicants will receive consideration for employment without discrimination on the basis of race, color, age, religion, sex (including pregnancy, gender, gender identity/expression, or sexual orientation), national origin, protected veteran status, disability, or genetic information (including family medical history).
We will provide reasonable accommodations for qualified individuals with disabilities. You may request an accommodation during the recruiting process with your Talent Acquisition team member.
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 26,159 AI roles we're tracking, AI Architect positions make up 1% of the market. At Cambium Learning Group, 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 $292,900 based on 108 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Cambium Learning Group AI Hiring
Cambium Learning Group has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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