Interested in this AI/ML Engineer role at EZMARKETING?
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About This Role
EZMarketing is a digital marketing agency helping small businesses across the U.S. We're a growing team that's passionate about results, innovation, and helping each other succeed. We are expanding into AI automations and AI\-powered client solutions.
Who We're Looking For – The Role
We’re looking for an AI Builder Integrator to bring AI technology and real\-world business results to small businesses. You’ll build secure dashboards, workflow automations, and AI agents that work in production for both inside our agency and for our clients.
If you love to architect AI agents, work flows and systems, then you will love this position. You should also be comfortable enough in front of clients to lead discovery, demonstrate solutions, and help close the deal.
You’ll help establish EZMarketing’s AI practice inside a business and the opportunity to shape the technology, processes, and team from the ground floor.
Key Responsibilities
You would be responsible for building and implementing AI systems inside our agency as well as integrating AI capabilities into client companies. It is critical that every solution is reliable, secure, and measurable.
You will work across internal operations and client engagements, this includes connecting AI models, data sources, automation platforms, and marketing tools into cohesive systems that drive efficiency and performance.
AI Implementation Responsibilities
- Identify, evaluate, and deploy AI tools and automations that improve operations across strategy, creative, media, analytics, and account management.
- Integrate AI systems with existing platforms (project management, CRM, analytics, Microsoft, Quickbooks and reporting tools).
- Build secure internal and client\-facing dashboards, internal tools, and workflow applications.
- Integrate AI solutions with systems through APIs, MCP’s, webhooks, databases, and automation platforms.
- Cost analysis of Model token usage, API fees, infrastructure costs, and ongoing support requirements.
- Lead or support client discovery sessions to identify process bottlenecks, requirements, risks, and measurable outcomes.
- Translate discovery findings into solution architecture, demonstrations, project scopes, and proposals.
- Support opportunities through the sales process by clearly explaining the proposed solution, value, limitations, and implementation approach.
- Establish reusable development standards, security practices, testing procedures, and deployment processes.
- Build and maintain internal AI workflows that reduce manual work, accelerate insight generation, and improve decision\-making.
- Establish internal standards for AI usage, prompt libraries, automation governance, and quality control.
- Train and support teams on AI tools, workflows, and best practices.
- Lead client workshops, discovery sessions, and solution demonstrations.
- Provide ongoing support, optimization, and expansion of AI programs.
Technical Requirements
- Integration \& Automation: Proven experience integrating AI services into production systems; event\-driven and scheduled automations; API\-first architecture.
- Data \& Infrastructure: Hands\-on experience with data lakes, modern data platforms, and programming languages; ability to design flows across raw, curated, analytics, and dashboard layers.
- MCP: Understanding of Model Context Protocol architecture; experience implementing MCP servers and clients for agentic workflows.
- Client Delivery: Manage technical projects from discovery through deployment; excellent communication with technical and non\-technical stakeholders; experience with marketing and IT teams preferred.
- Security \& Compliance: AI security best practices (prompt injection, adversarial input, access control, output validation, secure inference); GDPR/CCPA compliance; secure data handling.
Qualifications
- Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related technical field.
- 3\+ years of experience in technical integration, solutions engineering, AI engineering
- Proficiency in Python and at least one additional language (e.g., TypeScript, JavaScript, Go, or SQL).
- Hands\-on experience with LLM APIs such as OpenAI, Anthropic, Google Gemini, or comparable platforms.
- Experience with API development.
- Familiarity with marketing technology ecosystems (CRMs, CDPs, ad platforms, email/SMS, analytics, and reporting tools).
- Strong problem\-solving skills and a consultative mindset.
- Strong project ownership, including the ability to manage priorities, communicate risks, and move a build forward without constant direction.
- Insatiable curiosity: you test emerging AI tools and models independently and can explain plainly why one approach is better than another.
- Comfortable leading client discovery, presenting your work, and contributing to a sales conversation, even if closing is not your primary strength.
- Experience connecting AI solutions with CRM, ERP, document management, ticketing, accounting, or Microsoft 365 environments.
- Experience with AI evaluation frameworks, observability tools, automated testing, or model\-routing strategies.
Where You'll Work
Most team members work remotely, enjoying the flexibility it offers. If you're located near Lancaster, PA, you're welcome to work in the office. We provide all necessary technology—everything you need to hit the ground running.
Why You'll Love It Here
- Competitive Compensation with Bonus opportunities
- Collaborative, supportive team culture where your ideas truly matter
- Retirement Plan with 3% employer match
- Professional development opportunities
- Remote\-first flexibility with regular team syncs via Teams
How to Apply
Please submit your resume, a cover letter describing your experience with AI integration and client delivery, and links to relevant projects, repositories, or case studies.
*We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all team members.*
Pay: $70,000\.00 \- $80,000\.00 per year
Benefits:
- 401(k) matching
- Flexible schedule
- Paid time off
- Retirement plan
Application Question(s):
- Describe the AI Integrations you have built.
Experience:
- AI Development: 2 years (Preferred)
Work Location: Remote
Salary Context
This $70K-$80K range is in the lower quartile 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 EZMARKETING, 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. This role's midpoint ($75K) sits 65% below the category median. Disclosed range: $70K to $80K.
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.
EZMARKETING AI Hiring
EZMARKETING has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $80K - $80K.
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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