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About This Role
The Digital Marketing \& AI Enablement Intern supports the development and execution of digital marketing programs and AI\-powered solutions for clients and internal teams. This role combines traditional digital marketing responsibilities with emerging technology initiatives, including content development, social media management, analytics, AI agent support, workflow automation, and process documentation. The intern will gain practical experience working across marketing, technology, and operations while helping deliver innovative solutions that improve client outcomes, operational efficiency, and business performance. This is a fall, part\-time opportunity.
Responsibilities:
*Other responsibilities may be assigned.*
- Support digital marketing campaigns across web, email, social media, and other content channels under team guidance.
- Draft, schedule, and publish approved social content using tools like Monday.com, HubSpot, Hootsuite, Mailchimp, Pardot, Marketing Cloud, Emplify or Sprout.
- Assist with website content updates, page edits, UTM creation, QR code generation, image uploads, and basic formatting in CMS platforms.
- QA web pages for broken links, typos, mobile responsiveness, accessibility basics, and brand consistency.
- Monitor social channels for trends, engagement opportunities, and audience questions; escalate responses when needed.
- Assist with email list maintenance, basic segmentation, campaign setup, and QA testing.
- Pull basic performance metrics and help prepare campaign reports.
- Research digital trends, competitor activity, hashtags, keywords, and content ideas.
- Help organize assets, documentation, and content inventories.
- Pull basic performance metrics and help prepare campaign reports from tools like Google Analytics, HubSpot, or similar platforms.
- Collaborate with developers, designers, and digital team members to meet deadlines and support client deliverables.
- Assists in the design, configuration, testing, and optimization of AI agents by supporting workflow development, prompt creation, data organization, and performance evaluation to help deliver effective AI\-powered solutions.
- Help document agent logic, automation flows, and operational procedures.
- Research emerging AI tools, models, and automation capabilities.
- Work with operational and client teams to identify AI enablement and automation opportunities.
Skills/Qualifications :
- Current student or recent graduate pursuing computer science, information systems, marketing, communications, advertising, digital media, or a related field.
- Foundational understanding of digital marketing, social media, email marketing, and content creation.
- Foundational understanding of AI concepts, including large language models (LLMs), prompt engineering, automation workflows, and agent\-based systems.
- Familiarity with AI development or automation tools such as OpenAI, Microsoft Copilot, Zapier, Make, Power Automate, or similar low\-code/no\-code platforms.
- Understanding of workflow logic, API integrations, or system\-to\-system communication concepts.
- Familiarity with LinkedIn, Instagram, Facebook, X/Twitter, YouTube, and emerging social platforms.
- Basic familiarity with HTML, CSS, CMS platforms, Java, and website structure.
- Interest in UX, accessibility, GEO/SEO, analytics, and digital content best practices.
- Comfortable learning web tools, plugins, marketing and analytics platforms, and project management systems.
- Strong writing, editing, organization, and communication skills.
- Detail\-oriented, curious, collaborative, and able to manage assigned tasks in a hybrid work environment.
- Ability to collaborate effectively with technical and non\-technical team members.
- Basic understanding of cybersecurity principles, access control, and data protection best practices.
- Strong analytical and problem\-solving skills with the ability to think logically through technical challenges.
- Ability to document technical processes clearly and accurately.
- Interest in AI enablement within business environments, including internal operations and client\-facing applications, and eagerness to experiment responsibly with emerging AI and automation technologies.
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 G&S Business Communications, 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. Entry-level AI roles across all categories have a median of $110,000.
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.
G&S Business Communications AI Hiring
G&S Business Communications has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Raleigh, NC, US.
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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