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About This Role
About Chartis Interactive
We are a marketing and technology consultancy that works across a range of B2B and B2C clients. Our team is built on collaboration, high standards, and the belief that smart practitioners grow each other. We invest in the development of everyone on our team through a structured career development program, cross\-client knowledge sharing, and close collaboration with experienced internal experts.
What We Offer
- Competitive compensation and benefits
- Retirement savings plan (401K) and flexible vacation
- Real client variety with B2B and B2C accounts across industries, with meaningful strategic scope
- A culture that values practitioners: we expect leaders here to stay sharp, not just manage
- A team that genuinely invests in each other's growth
- Active engagement with AI tools and emerging capabilities as part of how we work, not as an afterthought
About the role
Chartis Interactive is looking for an SEO \& AI Search Consultant to support execution of AI Search (SEO/AEO/GEO) projects across key clients. Candidates need to have hands\-on experience in improving performance across organic search, owned branded assets, and integration with SMEs in paid search, content, development teams and analytics.
Senior Consultants are responsible for supporting SEO/AI Search strategy with hands\-on activities such as customer behavioral analysis, search insights, content strategy builds, technical performance assessments, external brand signals, creation of detailed content/technical briefs, competitive assessments, managing impact reporting, and supporting clients through implementation or strategic recommendations.
Candidates should possess a very strong understanding and curiosity of the evolving search landscape, and passion for testing new theories and approaches, to bring innovative solutions to our clients. Candidates should enjoy problem\-solving while developing their SEO and consulting skillsets.
Your Impact Every Day
- Understand clients’ organic growth and marketing objectives to support SEO/AI Search strategy execution led by SEO Lead/Manager \- as it relates to their products, industry, competitive landscape, and larger business goals.
- Conduct research and thematic segmentation based on intent, themes and other actionable insights using first\- and third\-party data sources, and/or workflows. This includes utilizing techniques around query\-fan out methods, Generative AI and evolving search behaviors.
- Analyze search landscape to evaluate intent, competitors, and successful tactics for visibility across Google organic search and AI Overviews, plus Gemini, OpenAI (ChatGPT), Anthropic (Claude), Perplexity, etc.
- Collaborate with team members on strategy development and execution roadmap planning \- including cross\-disciplinary teams.
- Map search behaviors to landing pages; audit client website for visibility against target keyword segments and determine which content should be optimized and where content needs to be created to address search needs.
- Perform technical performance assessments to identify optimization opportunities \- such as, Page Experience, Accessibility, Rendering and how UX is presented to target audiences.
- Provide detailed recommendations and strategic briefs, supporting content optimization and creation.
- Collaborate with client or agency copywriters to provide search insights, optimization requirements and growth opportunities.
- Audit client sites for technical issues impeding search visibility and provide recommended improvements to remedy issues.
- Support implementation and maintenance of structured data (JSON\-LD/schema.org) and AI crawler access (GPTBot, ClaudeBot, PerplexityBot, OAI\-SearchBot, etc.) to improve content visibility and citation in AI search results.
- Eagerness and proven methods to test/learn new approaches reflected within the change search landscape, such as advanced LLMs, GenAI and evolving platforms.
- Review paid search campaign and keyword performance as an input to SEO analysis and insight creation; work with paid search team for integrated search approach.
- Audit brands external digital footprint to improve overall brand presentation.
- Update reports and insights for performance using data from Google Analytics, Adobe Analytics, Google Search Console and third\-party tools (i.e. Semrush, AWR, Lumar, Sitebulb).
- Familiarity with AI visibility and citation\-tracking platforms (e.g., Profound, Scrunch, Semrush Enterprise, Semrush AIO, BrightEdge AEO) is a plus.
- Stay informed on new and emerging opportunities in search, best practices, and industry updates.
- Support development of insights and actions based on qualitative and quantitative data analysis.
Qualifications
- Passionate about Digital Marketing, SEO/Search, AI, and desire to learn, experiment and innovate, to not only make an impact for our clients, but drive personal growth
- 2\+ years of experience
- Strong understanding of technical and content elements that impact organic search performance rankings
- Eagerness to learn, grow and collaborate with peers across SEO, Digital Marketing, and cross\-functional teams
- Proficient in identifying issues and areas of improvement with SEO performance
- Experience with website analytics tools (e.g, Google Analytics, Adobe Analytics etc.)
- Working knowledge of HTML, CSS, JavaScript, structured data/schema markup (JSON\-LD), and how LLMs crawl, parse, and cite web content, as it relates to SEO and AI/LLM\-based search platforms, is a plus
- Knowledge of organic search ranking factors and search engine algorithms, with a desire to stay up\-to\-date on the latest in the Digital Marketing and SEO industry
- Previous experience working across multiple brands or enterprise websites highly desired
- Enjoy working in a fast\-paced, collaborative environment, exceptional organizational skills, multi\-tasking capabilities, and attention to detail
- Excellent communication skills (verbal and written)
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 Chartis Interactive, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Chartis Interactive AI Hiring
Chartis Interactive 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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