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About This Role
We're looking for a curious, self\-directed SEO Specialist who combines deep organic search expertise with a modern understanding of AI and how it's reshaping the search landscape. This role is the sole SEO practitioner within the agency — you'll own the SEO function across 15–20 client accounts spanning retainer engagements, project work, and new business proposals.
This isn't a "run the audit tool and hand over the spreadsheet" role. You'll be expected to interpret data, develop strategy, and communicate recommendations clearly to clients who rely on you as their trusted search advisor. You'll inherit a suite of custom\-built SEO tools and workflows, and we're looking for someone who can not only use them effectively but bring ideas to improve and extend them.
The ideal candidate is equal parts strategist, analyst, and communicator — someone who gets energized by digging into Search Console data, building a content roadmap, and then explaining the "so what" to a room of stakeholders.
What You'll Do:
Client Strategy \& Deliverables
You'll manage the full SEO lifecycle for a portfolio of 15–20 B2B clients, including:
\- Technical SEO audits and ongoing site health management — crawl analysis, indexation and crawl budget reviews, Core Web Vitals optimization, error status cleanup (3xx/4xx/5xx), and quarterly technical health reports.
\- On\-page optimization — title tags, meta descriptions, header hierarchy, image attributes, internal linking, and URL/canonical strategy. You'll implement these changes directly in client CMS platforms; template\-level changes are handled by our development team.
\- Keyword research, tracking, and analysis — keyword rank tracking reviews, cannibalization audits, SERP feature analysis, and performance trend identification.
\- Content strategy and audits — data\-driven content strategies with topic clustering and hub\-and\-spoke architecture, content decay audits to recover declining assets, and full website content audits evaluating on\-page elements.
\- Internal linking analysis — evaluating link equity distribution, anchor text relevance, orphaned pages, and crawl path efficiency.
\- Structured data / schema markup — reviewing existing implementations, identifying gaps, and building out schema (Organization, LocalBusiness, BreadcrumbList, FAQ, HowTo, Product, etc.) across client sites.
\- Competitor analysis — organic visibility benchmarking, content gap analysis, backlink profile comparison, and SERP feature ownership mapping.
\- Local SEO — Google Business Profile optimization, NAP consistency, citation management, local keyword targeting, and review strategy.
\- Link building — prospect identification, outreach strategy, content\-driven acquisition, broken link building, and backlink profile audits.
\- Performance reporting — Google Search Console reviews, keyword performance summaries, and monthly/quarterly reporting that translates data into clear narratives for client stakeholders.
\- Migration support — SEO planning for domain changes, platform migrations, and redesigns including redirect mapping, pre/post\-launch checklists, and monitoring.
\- SERP feature and AI visibility analysis — assessing featured snippet opportunities, AI Overview/SGE presence, and LLM discoverability (GEO).
AI Tooling \& Internal Development
\- Use large language models (Claude, ChatGPT, Gemini, or similar) as a daily part of your workflow — for analysis, content evaluation, data interpretation, and research acceleration.
\- Identify opportunities to automate repetitive workflows and improve data quality across the SEO function.
\- Bonus: contribute to internal tooling development using Python or AI\-assisted coding tools (Claude Code, Codex, Gemini CLI).
Content \& Thought Leadership
\- Contribute to agency content initiatives including blog posts, LinkedIn content, and internal knowledge sharing on SEO and AI trends.
\- Stay current on search algorithm updates, AI developments in search (SGE, AI Overviews, GEO), and emerging best practices — and translate that knowledge into actionable guidance for the team and clients.
Sales Support
\- Support new business efforts by contributing to proposals, conducting prospect\-facing SEO audits, and participating in pitch presentations when SEO expertise is needed.
What You'll Need (Required)
\- 2\+ years of agency SEO experience managing multiple client accounts simultaneously (5\+ years strongly preferred).
\- Demonstrated proficiency across technical, on\-page, and strategic SEO disciplines — you should feel comfortable with at least 75% of the deliverables described above from day one.
\- Strong working knowledge of core SEO tools:
\- Screaming Frog (or equivalent crawling software)
\- At least one of: SEMrush, Ahrefs, or Moz
\- Google Search Console
\- Google Analytics 4
\- Google Merchant Center
\- Looker Studio
\- Hands\-on CMS experience — you'll be implementing SEO changes directly, not just recommending them.
\- Proficiency with at least one major LLM (Claude, ChatGPT, Gemini, etc.) and practical experience using AI tools to enhance SEO workflows.
\- Excellent client\-facing communication skills — comfort presenting in monthly client meetings, fielding tough questions with composure, and translating technical SEO concepts into language that non\-technical stakeholders understand and act on.
\- Self\-directed work style — this is the sole SEO role in the agency. You need to manage your own priorities across 15–20 accounts without day\-to\-day oversight.
\- Bachelor's degree (or equivalent professional experience).
Preferred
\- 5\+ years of SEO experience, ideally in a B2B agency environment.
\- Python proficiency — ability to write scripts, work with data pipelines, or extend existing tooling.
\- Experience with AI\-assisted development tools such as Claude Code, OpenAI Codex, or Gemini CLI.
\- Familiarity with structured data / schema markup implementation (JSON\-LD).
\- Experience with Google Business Profile management and local SEO strategy.
\- Understanding of CRO/UX principles and how they intersect with organic search performance.
\- Exposure to GA4 configuration and troubleshooting — you don't need to be an analytics engineer, but you should know how to investigate a traffic spike and determine whether it's real or bot activity.
Who You Are
\- Curious by nature. You don't just run audits — you ask why the data looks the way it does and dig until you find the answer.
\- Comfortable with ambiguity. B2B SEO across diverse industries means no two clients look the same. You adapt your approach to the business, not the other way around.
\- A clear communicator. You can explain technical concepts without jargon and build trust with clients through transparency and confidence.
\- Friendly and approachable. You work closely with account specialists, content writers, developers, and paid advertising teammates. Collaboration isn't optional — it's core to how Aztek operates.
\- Excited about where search is going. AI is transforming this industry. You see that as an opportunity, not a threat, and you're eager to be at the forefront of that shift.
Cross\-Functional Collaboration
This role works closely with:
\- Account Specialists — coordinating client priorities, timelines, and communication.
\- Content Team — informing content strategy with keyword research, topic clusters, and optimization guidance.
\- Development Team — partnering on technical implementations, template\-level SEO changes, and site migrations.
\- Paid Advertising Team — aligning organic and paid strategies, sharing keyword and audience insights.
Why Aztek
This is an opportunity to own the SEO function at an agency that genuinely values innovation and gives you the freedom to shape how the work gets done. You'll inherit a mature set of tools and processes, work with a collaborative team that respects expertise, and have the space to grow the role in the direction your skills and curiosity take you. Aztek works with amazing companies, and we're looking for someone equally amazing to join us.
Job Type: Full\-time
Pay: $70,000\.00 \- $80,000\.00 per year
Benefits:
- 401(k)
- Dental insurance
- Employee assistance program
- Flexible schedule
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Professional development assistance
- Referral program
- Vision insurance
Experience:
- SEO: 2 years (Required)
Language:
- English (Required)
Work Location: Hybrid remote in Cleveland, OH 44114
Salary Context
This $70K-$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 ($75K) sits 55% below the category median. Disclosed range: $70K 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.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
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.
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