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About This Role
Position Overview
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The Senior AI Visibility/SEO Strategist is a client\-facing leader responsible for developing and executing enterprise SEO and AI Visibility strategies that drive measurable business growth for leading brands. This role partners closely with PartnerCentric's Client Services, Sales, Marketing, and Data \& Insights teams, while serving as a trusted advisor to client stakeholders, to develop strategic recommendations and maximize brand visibility across traditional search engines and AI\-powered search experiences.
The ideal candidate has extensive experience managing SEO and AI Visibility projects for large, complex websites and knows how to earn credibility with both technical and business stakeholders. This is not a traditional SEO role,we're looking for someone with proven, hands\-on experience leading Answer Engine Optimization (AEO) initiatives and improving AI search visibility leveraging enterprise platforms such as Peec, Athena, Profound, Goodie, or similar solutions.
This is a remote position, but candidates must reside and physically perform the work within the United States. We are only considering applicants who possess unrestricted, independent work authorization that does not now, or in the future, require employer sponsorship.
Key Responsibilities
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### Enterprise SEO Strategy
- Develop and execute enterprise SEO strategies that drive sustainable organic growth and measurable business outcomes.
- Perform technical SEO audits and deliver recommendations related to crawlability, indexing, site architecture, Core Web Vitals, structured data, internal linking, JavaScript rendering, and website performance.
- Partner with client technical teams to scope, prioritize, and implement SEO recommendations across complex websites.
- Lead keyword research, competitive analysis, content strategy, and search intent mapping.
- Stay ahead of search algorithm updates and translate industry changes into actionable client recommendations.
### AI Visibility \& Answer Engine Optimization (AEO)
- Develop and execute AI Visibility and AEO strategies that improve brand discoverability across ChatGPT, Gemini, Perplexity, Claude, Copilot, Google's AI Overviews, and other emerging AI search experiences.
- Measure and optimize AI search performance using platforms such as Peec, Athena, Profound, Goodie, or similar enterprise AI visibility tools.
- Analyze AI\-generated responses, citations, competitive positioning, and content opportunities to improve brand representation.
- Partner with internal teams and clients to create content strategies designed for both traditional search engines and AI\-powered answer engines.
- Continuously evaluate emerging AI search trends, tools, and best practices.
### Client Strategy \& Collaboration
- Serve as the SEO and AI Visibility subject matter expert for assigned enterprise clients.
- Present strategic recommendations and performance insights to marketing leaders, executives, and cross\-functional stakeholders.
- Partner closely with Client Services, Sales, Marketing, and Data \& Insights teams to support client growth, new business opportunities, and integrated marketing initiatives.
- Translate complex technical SEO concepts into clear business recommendations.
- Build trusted relationships with both business and technical stakeholders.
### Performance Analysis \& Reporting
- Monitor SEO and AI Visibility performance using Google Search Console, Google Analytics 4, enterprise SEO platforms, and AI visibility measurement tools.
- Develop reporting that focuses on business outcomes, search performance, AI visibility, and competitive insights.
- Identify opportunities for continuous optimization through data analysis and experimentation.
### Cross\-Functional Leadership
- Collaborate with Client Services, Affiliate Strategy, Content, Paid Media, Analytics, and Product teams to deliver integrated client strategies.
- Provide SEO guidance during website launches, redesigns, migrations, and major digital initiatives.
- Mentor junior team members and contribute to internal best practices, thought leadership, and service innovation.
Role Requirements
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- Bachelor's degree in Marketing, Communications, Business, Information Systems, Computer Science, or equivalent professional experience.
- 5–8\+ years of enterprise SEO experience managing large, complex websites.
- Demonstrated success working with enterprise brands and collaborating directly with engineering and development teams.
- Proven ability to build credibility with technical stakeholders while effectively communicating with marketing and business leaders.
- Must be Google Analytics 4 (GA4\) Certified.
- Nice to have: Adobe Analytics Certification, Moz SEO Essentials Certification, Semrush SEO Fundamentals, Ahrefs Certification,
- Hands\-on experience with enterprise SEO platforms such as Semrush Enterprise, Ahrefs, BrightEdge, Conductor, Botify, Screaming Frog, Sitebulb, or similar tools.
- Experience using Google Search Console, Google Analytics 4, Looker Studio, and other web analytics platforms.
- Proven experience leading Answer Engine Optimization (AEO) initiatives with measurable results.
- Hands\-on experience using AI Visibility platforms such as Peec, Athena, Profound, Goodie, or similar enterprise solutions.
- Strong understanding of how AI search platforms retrieve, synthesize, and cite brand information.
- Excellent written, verbal, presentation, and client communication skills.
- Highly organized with exceptional project management skills and the ability to manage multiple priorities in a fast\-paced agency environment.
Preferred Qualifications
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- Experience supporting affiliate, ecommerce, retail, finance, healthcare, travel, or other enterprise brands.
- Experience with website migrations, JavaScript SEO, schema markup, log file analysis, and enterprise CMS platforms.
- Familiarity with AI\-assisted content workflows and responsible use of generative AI technologies.
- Experience integrating SEO insights into broader digital marketing strategies.
A Week in the Life
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Monday: Review client performance dashboards, analyze AI Visibility trends, and prioritize strategic initiatives with Client Services.
Tuesday: Present technical SEO recommendations to a client's engineering team and collaborate with Data \& Insights to uncover new optimization opportunities.
Wednesday: Conduct an enterprise technical SEO audit, analyze AI citation performance using Peec or Profound, and develop recommendations to improve AI search visibility.
Thursday: Meet with client stakeholders to review quarterly SEO and AI Visibility performance, discuss competitive insights, and align on strategic priorities.
Friday: Research emerging AI search developments, mentor junior team members, contribute to internal thought leadership, and partner with Sales and Marketing on new business opportunities.
Benefits \& Perks
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- Competitive salary with performance\-based reviews and career growth opportunities.
- Comprehensive health, dental, and vision insurance.
- 401(k) with employer match.
- Flexible remote work with a collaborative, supportive team culture.
- Professional development stipend and ongoing learning opportunities.
- Generous PTO policy, including Flex Days and company\-wide holidays.
About PartnerCentric
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PartnerCentric is a leading performance marketing agency helping brands accelerate growth through Affiliate Marketing, Creator \& Influencer Marketing, Strategic Partnerships, Organic Search, AI Visibility, and emerging digital channels.
For more than 20 years, we've helped some of the world's most recognized brands build high\-performing partner programs that deliver measurable business results. Today, we're expanding beyond traditional marketing disciplines to help brands succeed in an evolving search landscape where consumers increasingly discover products and services through search engines, AI assistants, and conversational experiences.
We're a remote\-first company built on ownership, curiosity, innovation, and continuous improvement. We believe the best ideas come from people who challenge assumptions, embrace emerging technologies, and continuously seek better ways to solve client problems.
If you're excited about shaping the future of search and helping leading brands succeed across both traditional search engines and AI\-powered discovery, we'd love to hear from you.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At PartnerCentric, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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.
PartnerCentric AI Hiring
PartnerCentric has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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).
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 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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