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About This Role
About Aztek
Aztek is a B2B\-focused digital marketing agency based in Cleveland, Ohio. We partner with growth\-minded companies to deliver measurable results through strategy, execution, and continuous improvement. We value curiosity, ownership, and practical thinking over buzzwords.
About the Role
We’re looking for an SEO Specialist who understands that search is no longer just about rankings.
This role is for someone strong in core SEO fundamentals who is also actively exploring how AI is reshaping search — including AI Overviews, generative search, and large language model visibility (AEO/GEO).
You’ll manage SEO strategy and execution across multiple B2B clients while helping evolve how we approach search in an AI\-driven landscape. This is an opportunity to take real ownership of your work, influence direction, and help shape what modern SEO looks like inside our agency.
What You’ll Do
- Own SEO strategy and execution across a portfolio of client accounts
- Conduct technical audits and ongoing site health analysis (crawlability, indexation, site performance, etc.)
- Optimize on\-page elements including metadata, internal linking, site structure, and content alignment
- Perform keyword research and develop content strategies that align with business goals
- Analyze performance using tools like Google Search Console, GA4, and SEO platforms (Ahrefs, SEMrush, etc.)
- Collaborate with content, paid media, and development teams to execute recommendations
- Communicate insights, performance, and recommendations clearly to clients and internal stakeholders
AI, AEO \& GEO Responsibilities (Core to This Role)
- Analyze how AI Overviews and generative search features are impacting client visibility and traffic
- Identify opportunities for clients to appear in AI\-generated answers and summaries (AEO/GEO)
- Adapt content and SEO strategies for how information is retrieved and synthesized by AI systems
- Use AI tools to accelerate research, analysis, and workflow efficiency
- Contribute ideas and testing around how SEO should evolve as search behavior changes
What We’re Looking For
Core Requirements
- 3–6\+ years of SEO experience (agency experience preferred)
- Strong foundation in technical, on\-page, and strategic SEO
- Experience managing multiple clients or projects simultaneously
- Proficiency with core tools (Google Search Console, GA4, Screaming Frog, Ahrefs/SEMrush, etc.)
- Clear communication skills and ability to explain SEO concepts to non\-technical stakeholders
- Demonstrated curiosity about how AI is changing search (AEO, GEO, AI Overviews, etc.)
Nice to Have (Not Required)
- Experience testing or optimizing for AI\-driven search experiences (AEO/GEO)
- Familiarity with structured data, site migrations, or advanced technical SEO
- Experience using AI tools (ChatGPT, Claude, etc.) in SEO workflows
- Basic understanding of HTML, CSS, or JavaScript
- Interest in automation or scripting (Python or similar)
How You’ll Succeed in This Role
- You take ownership and follow through on your work
- You’re curious and ask “why” when analyzing data
- You’re comfortable navigating ambiguity and solving new problems
- You balance strategic thinking with execution
- You’re actively paying attention to where search is going, not just where it’s been
Our Culture \& Values
Aztek is built on five core values:
- Help Others
- Take Ownership
- Be a Trusted Advisor
- Get S\#!t Done
- Be Honest
Compensation \& Benefits
- Bonus potential based on performance
- 401(k) with company match
- Health, dental, and vision insurance
- HSA \& FSA options
- Paid parental leave
- Paid time off \+ holidays
- Tuition \& certification reimbursement
- Flexible work schedule
- Employee assistance program (EAP)
- Remote\-first culture
Why Aztek
- Real ownership over client strategy and outcomes
- A collaborative, no\-ego team environment
- Flexibility to test ideas and improve how work gets done
- Exposure to a variety of B2B industries and challenges
- A team that values being proactive, practical, and forward\-thinking
Pay: $74,000\.00 \- $80,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Employee assistance program
- Flexible schedule
- Health insurance
- Health savings account
- Paid time off
- Parental leave
- Tuition reimbursement
- Vision insurance
Application Question(s):
- Are you proficient in AI tools, AEO, GEO?
Experience:
- SEO: 2 years (Required)
Language:
- English (Required)
Work Location: Remote
Salary Context
This $74K-$80K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Aztek, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($77K) sits 54% below the category median. Disclosed range: $74K to $80K.
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 Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Aztek AI Hiring
Aztek has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Cleveland, OH, US. Compensation range: $80K - $80K.
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/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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