Interested in this AI/ML Engineer role at Priority group Services?
Apply Now →About This Role
AI Marketing Project Manager
Location: New York, NY 10036
Work Arrangement: Remote; preference for candidates based in New York who can work onsite 2–3 days per week if required
Contract: 3–6 months initially, with potential extension
Experience: 5–7+ years
Industry: Insurance
Job Function: Information Technology
Education: All education levels considered
Visa Sponsorship: Not available
Relocation Assistance: Not available
About the Role
We are seeking an experienced AI Marketing Project Manager to lead the planning, coordination, and delivery of AI-enabled initiatives across Marketing and Communications.
This role will serve as the bridge between Marketing business owners and Technology/AI development teams, translating business priorities into structured project plans and ensuring initiatives progress from ideation and scoping through proof of concept, pilot, implementation, adoption, and measurable business value.
The successful candidate does not need to be an AI engineer. However, they must have a practical understanding of AI concepts and be able to identify AI use cases, challenge assumptions, coordinate technical teams, and ensure appropriate privacy, security, governance, and responsible-AI considerations are incorporated into delivery.
The role will involve regular collaboration with distributed teams, particularly across the U.S., Ireland, and India, so flexibility for Europe-hours overlap is required.
Key Responsibilities
Project & Program Management
- Develop and maintain integrated project and portfolio plans covering scope, milestones, dependencies, resources, risks, and decision points.
- Manage multiple concurrent AI and digital initiatives with competing priorities.
- Establish accountability, track commitments, and ensure deliverables remain on schedule.
- Manage project budgets, resources, vendors, and delivery capacity.
- Monitor risks, issues, dependencies, and escalations throughout the project lifecycle.
Marketing & Business Partnership
- Partner with Marketing business owners to define business problems, requirements, acceptance criteria, KPIs, and expected outcomes.
- Translate marketing and business priorities into actionable technology initiatives.
- Maintain alignment between Marketing, Technology, leadership, and delivery teams.
- Support initiatives involving content, digital marketing, web, social media, CRM, branding, communications, and marketing workflows.
AI & Technology Delivery
- Identify and prioritize opportunities to apply AI and GenAI to marketing processes.
- Coordinate AI development teams, architecture, data, engineering, testing, security, and other technical stakeholders.
- Manage AI proofs of concept, rapid prototypes, pilots, testing, release readiness, and implementation.
- Support initiatives involving:
AI-assisted content creation AI agents for web properties AI-enabled CRM Automated content tagging Multilingual content publishing Marketing workflow automation
Responsible AI & Governance
- Ensure appropriate human-in-the-loop controls are incorporated into AI workflows.
- Coordinate privacy, security, legal, compliance, brand, and AI governance requirements.
- Consider data quality, data readiness, integrations, validation, and model/solution performance.
- Ensure appropriate documentation and governance controls are established before deployment.
Adoption & Change Management
- Develop adoption and change-management plans for new AI-enabled processes and technologies.
- Coordinate communications, training, champion engagement, and feedback mechanisms.
- Measure user adoption, business impact, and achievement of defined KPIs.
- Support transition from pilot/build into sustainable operational support.
Stakeholder & Executive Communication
- Lead project meetings, status reviews, and governance discussions.
- Communicate progress, risks, issues, decisions, dependencies, and escalations to senior stakeholders.
- Facilitate gateway reviews and ensure key decisions, assumptions, actions, and approvals are properly documented.
Required Qualifications
- 5–7+ years of project or program management experience, preferably within technology, digital transformation, data, automation, or AI-enabled initiatives.
- Experience working within a large, complex/global organization.
- Strong Agile project management and strategic planning experience.
- Experience coordinating business/marketing stakeholders with technology and engineering teams.
- Practical understanding of:
- AI/GenAI
- AI models
- AI agents
- AI governance
- Privacy and security
- Data quality and readiness
- AI solution validation/testing
- Experience managing POCs, rapid prototypes, pilots, testing plans, and technology delivery.
- Experience with project budget and resource tracking.
- Strong risk, issue, dependency, and stakeholder management skills.
- Excellent communication and executive presentation skills.
- Experience working with distributed teams across multiple time zones.
- Ability to provide regular overlap with Ireland/European working hours.
- PMP certification required.
Preferred Qualifications
- Insurance or consulting industry experience.
- Experience implementing AI into marketing or content workflows.
- Digital marketing, web, social media, creative, branding, or communications technology experience.
- Marketing technology / MarTech experience.
- Experience with multilingual content or publishing technology.
- Experience with AI-enabled CRM initiatives.
- Experience with AI agents for web properties.
- Agile, Scrum, SAFe, PRINCE2, or comparable certifications.
Pay: $50.00 - $70.00 per hour
Work Location: Hybrid remote in New York, NY 10036
Salary Context
This $104K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Priority group Services, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($124K) sits 41% below the category median. Disclosed range: $104K to $145K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Priority group Services AI Hiring
Priority group Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $145K - $145K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.