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About This Role
Beyond Media Global (BMG) is hiring an AI & Growth Operations Manager / Specialist for our Montvale, New Jersey office.
This is a hands-on role combining AI, business automation, growth operations, and digital marketing technology. You will work across client accounts to implement automated workflows, connect business systems, manage digital campaigns, and improve operational performance.
You will use Adeqo, BMG’s proprietary enterprise intelligence platform, and its AI agents alongside established marketing and business systems.
We are looking for someone who enjoys working with technology, understands how businesses generate and manage leads, and can take a project from concept to implementation.
About BMG
BMG develops proprietary technology to connect business systems, automate operations, and improve how work gets delivered.
Our Adeqo Context Engine (ACE) connects advertising, analytics, CRM, sales, and financial systems to provide a consolidated view of each client’s business. Adeqo uses this information to identify issues, support decisions, and coordinate AI agents that execute work across connected platforms.
You will operate Adeqo and its agents daily, build and maintain workflows, verify results, and work with our product and engineering teams to improve the platform.
3. Key Responsibilities
AI Operations & Automation
- Build, configure, and maintain AI-powered workflows for client and internal operations.
- Operate Adeqo and its AI agents to automate research, campaign execution, reporting, and recurring business processes.
- Connect CRM, advertising, analytics, and other business systems through APIs and integrations.
- Test automated workflows, investigate errors, and verify results before deployment.
- Identify opportunities to reduce manual work and improve operational efficiency.
- Work with BMG’s engineering team to develop new agent capabilities and integrations.
Growth & Marketing Operations
- Plan, execute, and optimize digital campaigns across paid media, email, web, and social channels.
- Manage marketing automation workflows, including lead capture, segmentation, nurturing, and lead scoring.
- Configure campaigns and customer journeys using HubSpot, Salesforce, or comparable marketing automation platforms.
- Monitor lead generation, conversion rates, campaign performance, and customer engagement.
- Support campaign execution across Google Ads, Meta, LinkedIn, and Microsoft Advertising.
- Coordinate with creative, SEO, and web teams to implement campaign improvements.
Data, Analytics & Reporting
- Configure and maintain GA4, Google Tag Manager, advertising pixels, and conversion tracking.
- Connect marketing, CRM, and business data to Adeqo.
- Analyze campaign attribution, lead progression, and customer journeys.
- Build and maintain dashboards using Adeqo, Looker Studio, and other reporting tools.
- Use SQL or comparable data analysis tools to investigate discrepancies and identify opportunities.
- Maintain data accuracy across connected systems.
Marketing Technology & Systems
- Configure and maintain CRM and marketing automation platforms.
- Build email automation workflows, audience segments, and lead management processes.
- Implement tracking tags, forms, and integrations across websites and landing pages.
- Make basic HTML/CSS changes to support tracking, email templates, and web content.
- Troubleshoot integration failures, data synchronization issues, and automation errors.
- Document configurations and establish repeatable workflows.
Client Delivery & Collaboration
- Manage assigned US client projects from planning through implementation.
- Participate in client meetings and present findings, progress, and recommendations.
- Coordinate with BMG’s engineering, design, SEO, and media teams.
- Prepare clear project specifications, timelines, and deliverables.
- Support technical audits and new-business assessments.
- Review completed work for accuracy and quality before client delivery.
4. Qualifications & Experience
The position may be filled at Specialist or Manager level depending on experience. Manager candidates should have at least five years of relevant experience and a record of independently managing client accounts or operational projects.
Required Qualifications
- At least three years of hands-on experience in digital operations, marketing technology, growth operations, or a related field.
- Practical experience implementing automation workflows using AI tools, marketing platforms, or business applications.
- Experience with CRM or marketing automation systems such as HubSpot, Salesforce, Marketo, or similar platforms.
- Working knowledge of digital advertising, lead generation, and campaign management.
- Experience with Google Analytics 4, Google Tag Manager, and performance reporting.
- Ability to troubleshoot technical issues and work with data from multiple systems.
- Strong organizational skills and the ability to manage multiple projects independently.
- Clear written and spoken English.
- Ability to work full-time on-site in Montvale, New Jersey.
Preferred Qualifications
- Experience with AI agents, APIs, MCP, or automation platforms such as n8n.
- Hands-on experience managing Google Ads, Meta Ads, or LinkedIn Ads.
- Experience with email marketing automation, lead scoring, and customer segmentation.
- Familiarity with Salesforce Marketing Cloud, Marketo, Eloqua, or Pardot.
- Working knowledge of SQL, Python, JavaScript, HTML, or CSS.
- Experience with Looker Studio or business intelligence platforms.
- Experience with attribution modeling and CRM data integration.
- Agency or consulting experience supporting multiple client accounts.
5. Working Arrangement
This is a full-time, on-site position at BMG’s Montvale, New Jersey office.
Standard US business hours apply, with occasional meetings outside normal hours to coordinate with colleagues in Asia and Australia.
Compensation and benefits will be discussed during the interview process.
6. About Beyond Media Global
Founded in 2009, Beyond Media Global (BMG) is an independent technology and growth company with offices across the United States, Asia, and Australia.
BMG combines digital marketing, enterprise intelligence, and AI automation through its proprietary platforms, including Adeqo, powered by the Adeqo Context Engine (ACE), and Pable.ai, its agentic AI technology.
BMG is a Google Premier Partner, Klaviyo Partner, Meta Badged Partner, and HubSpot Solutions Partner.
Pay: $90,900.00 - $110,000.00 per year
Benefits:
- Dental insurance
- Health insurance
- Paid time off
Work Location: In person
Salary Context
This $90K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Beyond Media Global LLC - New Jersey, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $90K to $110K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Beyond Media Global LLC - New Jersey AI Hiring
Beyond Media Global LLC - New Jersey has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Montvale, NJ, US. Compensation range: $110K - $110K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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