Interested in this AI/ML Engineer role at gemini agency?
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About This Role
Ever wonder why some businesses show up everywhere while others are impossible to find? The answer is changing faster than ever. Search engines, AI platforms, and consumer behavior are evolving daily, creating new opportunities for businesses to stand out.
If that sounds interesting to you, keep reading. This could be the perfect opportunity to launch your career in digital marketing.
Launch Your Marketing Career at Gemini Agency
Gemini Agency is looking for an Organic Search Coordinator to join our growing team. This is an entry\-level role designed for someone who wants hands\-on training from experienced marketers while building real\-world skills in SEO, AI search visibility, analytics, automation, and the future of digital marketing.
At Gemini, we believe skills can be taught, but character matters most. That's why we hire people who embody our core values of Trust, Commitment, Excellence, and Integrity. If that sounds like you, here's what you can expect in this role.
What You'll Do
- Help clients improve their visibility across Google, Bing, ChatGPT, Gemini, Perplexity, Claude, and other emerging discovery platforms
- Support core SEO initiatives, including keyword research, website optimization, technical SEO tasks, and performance reporting
- Analyze website and search performance data to identify opportunities for improvement using tools such as Google Analytics, Google Search Console, and SEMrush.
- Create monthly performance reports that highlight the business impact of clients' SEO and AI search efforts.
- Learn how content, website structure, authority signals, and user experience influence both traditional search results and AI\-generated recommendations
- Use AI tools and automation workflows to improve efficiency, uncover insights, and support marketing initiatives
- Work directly in client CMS platforms and page builders to publish new and optimized content.
- Collaborate with SEO specialists, content creators, account managers, and project managers while staying current on emerging trends in search, AI, and digital marketing.
- Assist content writers with drafting and editing content.
- Carry out meaningful digital marketing work for real businesses in a fast\-moving industry.
- Assist the team with email creation as needed.
What We're Looking For
- Exceptional attention to detail
- Excellent communicator, both written and verbal
- Strong organizational and time management skills
- A process\-driven mindset
- Curiosity and a passion for learning
- Strong problem\-solving and analytical thinking skills
- Ability to work independently and as part of a team
- Comfort with technology and a willingness to learn emerging AI tools
- Bachelor's degree in Marketing, Communications, Business, Technology, or a related field preferred
- Familiarity with SEO, websites, analytics, or AI tools is a *plus, but not required*
- Experience with WordPress or other common CMS platforms is a *plus, but not required.*
What You'll Receive
- Hands on training from experienced digital marketers
- Exposure to cutting edge AI tools, workflows, and automation platforms
- Ongoing professional development and mentorship
- Flexible work environment with hybrid work options
- Competitive compensation, bonus opportunities, and comprehensive benefits
- A supportive team environment where people genuinely enjoy working together, sharing ideas, celebrating wins, and having fun along the way
Why Gemini?
Founded in 1992, Gemini Agency has built a reputation for helping businesses grow through smart marketing, strong partnerships, and a commitment to results.
We believe the future of marketing belongs to people who embrace both innovation and accountability. If you're looking for a place where you can learn, grow, and help shape what's next, we'd love to hear from you.
Salary Context
This $45K-$55K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At gemini agency, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($50K) sits 72% below the category median. Disclosed range: $45K to $55K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
gemini agency AI Hiring
gemini agency has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Edina, MN, US. Compensation range: $55K - $55K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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