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About This Role
AI Platform Engineer
Camping World Holdings \| AI \& Cloud Operations \| Chicago, IL (Hybrid)
About the Role
Camping World Holdings is seeking an AI Platform Engineer to bridge our current cloud and DevOps operations with our next\-generation AI\-powered development platform. This is an individual contributor role on the AI \& Cloud
Operations team — you’ll own the platform underpinning our DevOps practice (AKS, CI/CD, IaC, and operational excellence) while equally driving AI development pipeline strategy, MCP server infrastructure, and the modernization
of our delivery toolchain and developing AI Agents using within various AI platforms such as Claude, Gemini, Sierra and DevRev. This is a hands\-on role with DevOps experience, cloud knowledge and previous AI Agent development and deployment
What You’ll Own
Key Responsibilities
Production Readiness Assessment
- Receive prototype applications and conduct structured assessments covering security posture, data model integrity, authentication and authorization flows, input validation, dependency hygiene, and test coverage quality
- Identify and document failure patterns endemic to AI\-generated code including hardcoded secrets, flat or unindexed schemas, missing error handling, and hallucinated or unpinned dependencies
- Produce clear remediation plans with prioritized findings, working within the architectural standards set by the Full\-Stack Systems Architect
- Hands on experience building Agentic Agents in Gemini/Vertex, OpenAI, Claude or similar tools
Code Remediation \& Hardening
- Refactor and harden AI\-generated codebases to meet enterprise production standards across frontend frameworks, backend APIs, data modeling, and authentication systems
- Replace or rewrite AI\-generated test suites against human\-reviewed acceptance criteria, ensuring coverage reflects real production behavior rather than checkbox validation
- Use AI\-augmented development tools (Cursor, Claude Code, GitHub Copilot) to accelerate remediation work while exercising independent judgment on when AI tooling is introducing new risk
Security \& Compliance
- Identify and remediate common security vulnerabilities including injection flaws, broken authentication, insecure direct object references, and exposed secrets or credentials
- Implement and validate secure authentication and authorization patterns in accordance with enterprise security
- policies
- Ensure applications meet CI/CD pipeline requirements and version control standards prior to production deployment
Pattern Recognition \& Knowledge Management
- Document recurring AI code failure patterns and contribute to a growing internal knowledge base
- Feed pattern intelligence back upstream to improve prototype quality at the source, collaborating with developers and architects to reduce remediation burden over time
- Stay current on AI\-assisted development tooling, emerging failure modes, and production readiness best practices
Collaboration \& Communication
- Partner with application teams, architects, and business stakeholders to align on readiness criteria and timelines
- Communicate technical findings clearly to both engineering and non\-technical audiences
- Provide guidance and thought leadership on responsible use of AI development tools within the engineering organization
Qualifications
Core Engineering
- Strong full\-stack fundamentals across at least one major frontend framework (React, Vue, Angular), backend API development, relational data modeling, and authentication systems
- Proficiency in Python, JavaScript/TypeScript, and at least one additional backend language
- Solid understanding of RESTful API design, database schema design, and ORM patterns
- Experience with version control discipline, branching strategies, and code review processes
AI Code Failure Pattern Recognition
- Strong ability to identify AI\-generated code failure modes: hardcoded credentials, hallucinated libraries, flat schemas, checkbox tests, missing error handling, and over\-reliance on happy\-path logic
- Practical experience evaluating AI tool output for correctness, security, and production viability
- Ability to distinguish between AI tooling as an accelerant versus AI tooling compounding a problem
Security \& Production Standards
- Familiarity with OWASP Top 10 and common application security vulnerabilities
- Experience implementing or validating secure authentication flows (OAuth 2\.0, JWT, session management)
- Understanding of CI/CD pipeline requirements, environment configuration, and secrets management
Testing \& Quality
- Experience writing and reviewing test suites with meaningful coverage — unit, integration, and end\-to\-end
- Ability to evaluate test quality and replace AI\-generated checkbox tests with coverage that reflects real production behavior
Communication \& Collaboration
- Strong written and verbal communication skills with the ability to present technical findings to non\-technical stakeholders
- Proven ability to work both independently and within cross\-functional engineering teams
- Self\-starter with strong problem\-solving skills and a bias toward documentation and knowledge sharing
Education \& Experience
- Bachelor’s degree in computer science, Information Systems, or a related field; equivalent professional experience considered
- 5\+ years of full\-stack software development experience
- 3\+ years of hands\-on experience with AI\-augmented development tools in a professional context (Cursor, Claude Code, GitHub Copilot, or equivalent)
- 2\+ years of experience in application security, code review, or production engineering disciplines
- Demonstrated experience identifying and remediating vulnerabilities in production codebases
Strongly Preferred
- Hands\-on experience building MCP servers or LLM tool\-use integrations.
- Hands\-on experience building agentic agents in Gemini/Vertex, OpenAI, Claude, or other AI Tools.
- Expertise in CI/CD pipelines.
- Experience with Kong, Kuma, or comparable API/service mesh tooling.
- Exposure to AI\-assisted development platforms (Lovable, Cursor, Claude Code).
- Experience with monitoring and observability platforms (Dynatrace).
How You’ll Work
- This role is part of the AI \& Cloud Operations team, reporting to the Sr. Director of AI \& Cloud. You’ll collaborate daily with platform engineers, application developers, and security and compliance stakeholders.
- We operate with a high degree of autonomy — you’ll be expected to drive decisions, document your work, and bring others along.
- We use best in class AI\-forward tooling and we expect our engineers to model and advance that culture.
General Compensation Disclosure
The pay range for this role considers several factors in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. At Camping World, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the factors stated. A reasonable estimate of the current range is listed below.
Pay Range:
$162,600\.00\-$249,400\.00 Annual
In addition to competitive pay, we offer Paid Time Off, 401(k), an Employee Assistance Program, Good Sam Roadside Assistance, discounts, paid parental leave (if eligibility is met), Tuition Reimbursement (if eligibility is met), and on the job training opportunities. Full\-time associates are offered a comprehensive benefit package including medical, dental, vision and more! Part\-time associates are offered access to dental \& vision coverage! For more information please visit: www.mycampingworldbenefits.com
We are an equal employment opportunity employer. The Company's policy is not to discriminate against any applicant or employee based on race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, veteran or uniformed service\-member status, genetic information, or any other basis protected by applicable federal, state, or local laws.
Salary Context
This $162K-$249K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Camping World, 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. Disclosed range: $162K to $249K.
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.
Camping World AI Hiring
Camping World has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Lincolnshire, IL, US. Compensation range: $140K - $249K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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