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About This Role
As VP of AI Enablement at Razorfish, you will lead our AI enablement strategy across three pillars: (1\) internal productivity and automation, (2\) client innovation and our AI story, and (3\) platform AI and partnerships (Google, Meta, Amazon). The role is both hands\-on and strategic — you set the direction and you build alongside the team.
We have already built and shipped a suite of agentic AI web applications, in production, spanning the media\-planning lifecycle. That platform is not the whole job — it is the proof that we can build at the frontier and the engine that helps enable all three pillars. Your mandate is to grow that capability, deepen adoption, sharpen our market story, and turn platform partnerships into advantage.
We are looking for someone rare: hands\-on enough to build alongside the team and strategic enough to set the direction. The ideal hire is already on the frontier of agentic AI — someone with the ambition and ability to have built a platform like this in the first place. At minimum, you can develop a clear vision for how to advance all three pillars, grow adoption, and keep the work evolving as the underlying tools advance.
The three pillars
The role is organized around three pillars. The platform we've built helps enable all three — it is the means, not the mission.
- Pillar 1 — Internal productivity \& automation. Build and scale internal agentic tools and automations that replace manual, spreadsheet\-bound workflows and make every craft faster and more accurate. A suite of agentic apps already in production is the flagship proof point and the platform this pillar grows from.
- Pillar 2 — Client innovation \& the AI story. Turn AI capability into client\-facing advantage — pitch wins, sharper deliverables, and new products — and build and tell our AI story in the market: the thought leadership, positioning, and narrative that win business and make us known for this.
- Pillar 3 — Platform AI \& partnerships. Stay ahead of platform\-native AI across major platforms such as Google, Meta, and Amazon, guide teams to use it well, and build the partner relationships that unlock data access, co\-development, and advantage — including connecting our tools directly to platform APIs.
- Own the AI enablement strategy. Develop and own a clear vision across all three pillars: internal productivity and automation, client innovation and our AI story, and platform AI and partnerships.
- Build, hands\-on. This is a builder's role. You will personally prototype and ship tools using modern agentic coding environments, stand up multi\-agent workflows, and steer what the team builds — not just manage a roadmap from a distance.
- Architect multi\-agent systems. Decide when a problem is best solved with a single well\-scoped prompt versus a deterministic multi\-agent pipeline with auditable, computed outputs.
- Grow our agentic platform. Evolve and scale our suite of agentic apps already in production as the platform that helps power all three pillars — it serves the pillars; it is not the whole mandate.
- Pillar 1 — drive internal adoption. Partner with leaders across SEO, paid search, paid social, programmatic, ad ops, strategy, and media ops to find high\-leverage use cases, replace brittle spreadsheet workflows, and build the literacy and documentation that make adoption stick.
- Pillar 2 — client innovation and the AI story. Turn AI capability into pitch wins, sharper client deliverables, and new products — and build and tell our AI story to clients and the market through thought leadership and a credible external voice.
- Pillar 3 — platform AI and partnerships. Stay ahead of platform\-native AI across major platforms such as Google, Meta, and Amazon, guide teams on best practices, and build the partner relationships that unlock data access, co\-development, and advantage — including connecting our tools directly to platform APIs.
- Make build\-vs\-buy calls deliberately. Form and maintain a clear point of view on where platform\-native and enterprise AI tools are the right choice versus where custom systems we build outperform them.
- Govern data, cost, and risk. Keep the work defensible: metered\-API cost controls, auditable outputs, per\-user authentication, and strict confidential\-client\-data discipline.
- Develop the team and measure impact. Mentor builders and analysts, raise the organization's AI literacy, and establish KPIs for productivity, quality, cost savings, adoption, and client/market impact — reporting to senior leadership.
- Hands\-on agentic building (strongly preferred, heavily weighted). Demonstrated experience building with agentic coding environments — modern AI coding agents such as Claude Code or Codex, Cursor, or equivalent — with real tools or pipelines you have personally shipped and that real users use. A portfolio beats a résumé line here.
- Frontier fluency. You are already on, or visibly ready to jump to, the frontier of agentic AI — not someone who has read about it. You can speak credibly about what changed in the tooling in the last few months and what you adopted because of it.
- Multi\-agent / LLM systems understanding. Practical grasp of multi\-agent orchestration, prompt and context engineering, schema\-validated LLM output, and why deterministic computed numbers (not LLM arithmetic) make a deliverable defensible.
- Strategic range. 8\+ years in digital marketing, marketing operations, product, or business transformation, with a track record of driving adoption of new technology and workflows — and the ability to set vision, not just execute tasks.
- Leadership without (and with) authority. 3\+ years leading or enabling teams across disciplines; able to influence crafts that don't report to you and to mentor builders who do.
- Marketing\-workflow depth. Real understanding of how paid search, SEO, paid social, programmatic, ad ops, strategy, and media ops actually work, so you can spot where AI creates leverage.
- Product and program instincts. Comfortable owning the development and deployment of internal tools end to end — scoping, shipping, measuring, iterating.
- Storytelling and thought leadership. Able to build and tell a compelling AI story — internally, to clients, and in the market — and to represent the agency credibly as an innovator.
- Partnership and stakeholder skills. Able to build productive relationships with major platform partners (e.g., Google, Meta) and to influence senior internal stakeholders.
- Change management and communication. Able to build genuine enthusiasm for new tools while addressing real resistance, and to translate technical work into business impact for senior stakeholders.
- Analytical, outcome\-driven mindset. You measure impact and tell the truth about it.
- Nice to have. Familiarity with cloud deployment (e.g. GCP Cloud Run), modern LLM APIs, the Model Context Protocol (MCP), and cost governance for metered AI/data APIs.
Education
BA/BS in Marketing, Business, Computer Science, Information Systems, or related field. Equivalent demonstrated capability — especially a portfolio of shipped agentic tools — is weighed at least as heavily as the degree.
Salary Context
This $0K-$0K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Publicis Groupe, 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($1) sits 100% below the category median. Disclosed range: $1 to $2.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Publicis Groupe AI Hiring
Publicis Groupe has 41 open AI roles right now. They're hiring across AI/ML Engineer, AI Engineering Manager, Data Scientist, AI Architect. Positions span Miami, FL, US, Boston, MA, US, New York, NY, US. Compensation range: $0K - $299K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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