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About This Role
AI Business Systems Engineer \- REMOTE – C\-4 Analytics
C\-4 Analytics is a fast\-growing, private, full\-service digital marketing company that excels at helping automotive dealerships increase sales, increase market share, and lower cost per acquisition. C\-4 Analytics is committed to developing innovative solutions for every dealer in every market and to providing the highest levels of accountability and customer service. We are currently hiring for an AI Business Systems Engineer, REMOTE to serve as a cross\-functional builder and thought partner for applying AI across the company.
This is a business\-first role for someone who deeply understands how organizations operate, can identify high\-leverage opportunities for automation and intelligence, and has the technical fluency to design and implement solutions end\-to\-end.
The ideal candidate combines strong business judgment, an engineering mindset, and an expert\-level understanding of modern AI capabilities.
If you are unable to complete the application due to a disability, contact us to ask for accommodation or an alternative application process.
The Gig: Your Mission
- The AI Visionary: Be the go\-to expert applying AI across Sales, Marketing, Ops, Finance, and beyond.
- The Workflow Wizard: Map processes, spot bottlenecks, and eliminate manual effort with brilliant AI solutions.
- The Systems Architect: Proactively propose and translate ambiguous business needs into structured, end\-to\-end system designs.
- The Hands\-On Builder: Design and execute game\-changing, AI\-powered workflows using modern automation tools and APIs.
A Day in the Life:
- Build smarter solutions: Apply AI for decision support, content generation, classification, and data enrichment.
- Integrate systems: Ensure internal systems (CRM, data platforms, cloud tools) are seamlessly connected.
- Own the solution: Take projects from lightbulb moment to production, ensuring reliability and business relevance.
- Stay current: Serve as the internal expert on the latest AI models and tooling.
What You Need to Succeed: The Rare Trifecta
This role requires a combination of all three:
- Business Process Strength: Strong intuition for how a fast\-paced organization operates, comfort with senior leaders, and clear communication on goals and tradeoffs.
- Engineering Mindset: Thinks in systems (inputs/outputs/failure modes), is fluent in APIs and data structures, and drives relentlessly for reliability and scalability.
- AI Expertise: Deep, hands\-on understanding of modern AI models, knowing exactly where AI adds maximum leverage, and experience shipping AI into *production* workflows.
Core Background
We are looking for hands\-on experience in real, imperfect operating environments.
- Experience: Typically 4\+ years of professional experience owning systems end\-to\-end. Demonstrated history of shipping solutions that other teams rely on daily.
- We Value: Consulting roles with implementation responsibility, internal tools/business systems teams, and experience in startup/scale\-up environments.
- We Are Not Seeking: Pure ML research profiles, AI "evangelists" without execution depth, or product managers who do not build/implement.
Hard Qualifiers (Non\-Negotiable)
- Experience building cross\-functional business systems, not isolated tools.
- Ability to go from whiteboard concept to live, production workflow.
- Focus on business impact and ROI, not novelty for its own sake.
- Comfort pushing back on weak ideas, even with senior stakeholders.
Soft Qualifiers (Equally Important)
- Low ego and high ownership.
- Impatience with unnecessary process and wasted effort.
- Calm, structured thinking in ambiguous situations.
- Practical decision\-making over "perfect" architecture.
Specific Skills \& Expertise
- Must\-Have: Proven track record in building cross\-functional systems, strong familiarity with AI models, experience with automation platforms and APIs, and excellent communication.
- Nice\-to\-Have: Background in operations/consulting, experience with CRMs/data platforms, light scripting (Python/JavaScript).
What This Role Is NOT:
- Not a research or theory role—this is about *building*.
- Not a departmental analyst—this is cross\-functional.
- Not a pure software engineering role—this is business\-driven systems building.
Salary: $100,000 to $130,000 USD per year (competitive and negotiable). Benefits: Health insurance, retirement plans, professional development, and unlimited paid time off!
Check out our careers and culture page for more details.
Note: Understanding the Different Between AI Automation Specialist and AI Business Systems
AI Automation Specialist \= *Execution\-focused operator*
Builds, maintains, and scales AI\-driven automations that make teams faster and processes cheaper today.
AI Business Systems Engineer \= *Systems architect \& integrator*
Designs durable, governed AI systems that become part of C\-4’s core business infrastructure.
*“The Automation Specialist makes us faster this quarter.*
*The Business Systems Engineer makes sure we don’t regret it next year.”*
AI Automation Specialist
AI Business Systems Engineer
Owns workflow\-level execution
Owns system\-level design
Delivers fast, reliable automations
Optimizes services over time
Heavy on shipping \& iteration
Heavy on mapping \& judgment
Thinks in concrete task chains
Thinks in org\-wide flows
The Vibe at C\-4 Analytics:
C\-4 Analytics takes the guesswork out of advertising. We don’t over\-promise: we over\-deliver. We provide real value to our clients because we really value them as partners. We love Google and Facebook, but *also* love Instagram and Bing. We innovate, educate, and instigate. We are forward\-thinking, but we learn from the past. We are results\-driven, and our strategies drive results. We love the practical applications of psychology to marketing, but we aren’t above a good practical joke. We are team players, but we love to crush our competitors. We create an environment of respect, and we respect the environment. We are the brains and the good looks. We are very humble. We are nerds, but cool, likable nerds. We are never gonna give you up. Never gonna let you down. We are all work and all play. We calculated that only 15\.8% of visitors who started this paragraph would actually read this far down. We are C\-4 Analytics
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Salary Context
This $100K-$130K 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 C-4 Analytics, 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 ($115K) sits 37% below the category median. Disclosed range: $100K to $130K.
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.
C-4 Analytics AI Hiring
C-4 Analytics has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $130K - $130K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 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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