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About This Role
Vice President Director, Technology \- AI Customer Solutions
Digitas is seeking a Vice President Director, Technology \- AI Customer Solutions to drive technical innovation at the intersection of generative AI, data, and digital marketing. In this pivotal role, you will partner closely with client teams, internal teams and practices to analyze their processes, uncover pain points, and map out opportunities for AI transformation. You’ll explore and evaluate diverse sources of information \- including structured data and unstructured content \- to design and create bespoke agentic solutions tailored to each unique challenge. While generative AI will be a core focus, your toolbox will also include automation technologies, process/workflow optimization, and the development of new products, platform features or agents.
As an expert in Prompt Engineering, you will bring a deep understanding of how to harness and fine\-tune generative models for optimal results. Your relentless curiosity and boundary\-pushing mindset will drive you to experiment boldly and identify unconventional approaches that deliver measurable business impact. You will act as a thought leader and hands\-on innovator, leveraging the latest advancements in technology to create practical, scalable solutions for our clients and internal teams.
You will serve as a key client partner— contributing to ongoing Digitas AI product and platform development, addressing customer inquiries, and ensuring clients receive exceptional value and support. Your people skills will be equally important as your technical knowledge. Your top\-notch communication skills and client\-centric mindset will foster strong relationships, solve real client problems leading to client satisfaction, and encourage long\-term engagement with our AI solutions.
Why Join Us?
You’ll play a pivotal role in shaping the future of digital experiences and AI\-powered solutions for Fortune 500 clients, leveraging cutting\-edge technology in a collaborative, agency environment.
Key Responsibilities:
- Supporting organic growth or new business activities such as RFP responses and client pitch development, presentation and follow\-up
- Building client relationships and trust, and act as an AI solution expert for their team
- Translate complex technical solutions into easy\-to\-understand concepts while conveying value and need
- Conducting client walkthroughs of requirements, technology recommendations/POVs and/or live demonstrations of working functionality and capturing feedback
- Partner with client \& project teams to define technology roadmaps
- Directing and driving the end\-to\-end discovery, strategy, and delivery of AI\-powered projects using our bespoke generative AI platform
- Gather requirements, architect, prototype, and implement innovative solutions across diverse business use cases
- Collaborate with cross\-disciplinary teams—including designers, developers, and strategists—to ideate, develop, and launch new products and experiences
- Partner with clients to understand their evolving generative AI needs and proactively identify opportunities where generative AI or technology can support their business goals
- Lead requirements gathering, use case documentation, and define acceptance criteria in partnership with clients, stakeholders and development teams
- Identify opportunities to expand and enhance core platform and collaborate with platform development team to implement
- Guide teams, including Product Managers, Engineers, and QA Analysts, across the full software development lifecycle (SDLC), ensuring high\-quality, timely delivery
- Conduct technology audits, vendor analyses, and help define product roadmaps and technical strategies
- Be a subject matter expert in prompt engineering and generative AI best practices, offering hands\-on guidance and training to clients to maximize the value of the platform
- Present complex technical concepts to both technical and non\-technical audiences with clarity and confidence
- Stay current with the latest advancements in emergent technologies, protocols, frameworks, best practices and compliance as related to generative AI (for example: CP2A, Responsible AI, MCP, and EvalOps), while identifying opportunities for new processes, functionality and improvement. Maintain strong organizational, time\-management, and risk\-mitigation practices in a dynamic, fast\-paced environment
Flexibility working with international timezones
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We’re looking for strong, impactful work experience, which typically includes:
- 9\+ years in a Product Manager, Product Owner, or Business Analyst role, with significant digital/technology project experience
- 4\+ years consulting or working in a digital agency environment
- Bachelor’s degree (or equivalent) in Business or Technology related field preferred
- Robust expertise in Artificial Intelligence and Generative AI, with hands\-on experience in designing, deploying, and optimizing solutions
- Proven capability in system/business analysis and driving product development from ideation to launch
- Proficiency in modern AI platforms, tools, and the digital/software development lifecycle
- Excellent analytical, creative problem\-solving, and critical thinking skills
- Exceptional communication, presentation, and interpersonal abilities
- Demonstrated leadership in managing multidisciplinary teams and fostering innovation
- Adaptability to handle risk, change, and feedback while maintaining composure under pressure
- Outstanding written and verbal communication skills to support interaction with a variety of team members at different seniority levels and varying degrees of technical understanding
- Experience working directly with senior level clients to gain trust and establish rapport for product management, technology assessment and development work required
- Outstanding technical documenting skills
- Experience using Atlassian tools (JIRA, Confluence) required
- Experience working with remote teams in different time zones required
- Flexibility to travel as needed required
Ready to drive innovation at the forefront of AI? Apply today!
2026\-152783
Salary Context
This $183K-$207K range is above the median 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 ($195K) sits 11% below the category median. Disclosed range: $183K to $207K.
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 Boston pay a median of $210,000 across 97 tracked positions. That's 3% below 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 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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