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About This Role
Associate Director, Digital Product Management – AI Orchestration
Digitas is seeking a skilled and motivated Associate Director, Digital Product Management – AI Orchestration to enhance agency operations and client projects by applying generative AI and data to digital marketing.
You’ll join a team that’s building and maturing our ever\-accelerating bespoke AI platform in service of the rest of the agency. The team creates, deploys, and supports custom AI agents that increase the efficiency and productivity of our internal teams, while keeping them on the cutting edge of serving our clients. In this pivotal role, you will partner closely with internal teams to analyze processes, uncover pain points, map out opportunities, apply technical strategy, and deliver solutions for AI transformation.
While generative AI will be a core focus, your toolbox will also include automation technologies and process/workflow optimization.
Key Responsibilities:
- Represent the Digitas AI team to internal partners, stakeholders, and clients.
- Partner with internal teams and SMEs to deliver AI solutions for agency teams such as Strategy, Data and Analysis, Media, Experience Design, Project Management, Social and our cross\-capability client suites (Total Commerce, CRM, Creative Experience).
- Drive the discovery and delivery of AI\-powered projects using our generative AI platform.
- Collaborate with Product Managers, engineers, and stakeholders to translate business needs into clear technical requirements and actionable tasks.
- Collaborate with cross\-disciplinary teams to ideate, develop, and launch new agents and experiences.
- Create agents and orchestrations on your own and in collaboration with SMEs and engineers
- Provide hands\-on technical oversight, troubleshooting, and problem\-solving as new features and enhancements are developed.
- Help to shape intake, planning, and delivery processes that make work mature, scalable, sustainable, and impactful.
- Build and execute rollout, training, support, and maintenance processes and practices.
- Stay current with generative AI, MarTech, and related technologies to inform best practices and recommend improvements.
- Present complex technical concepts to both technical and non\-technical audiences with clarity and confidence.
- Report to the Vice President Group Director, Technology, Services \& Suites.
Who You Are:
- Technically adept and passionate about Generative AI (and related tech), innovation, and delivering high\-quality solutions.
- A leader and a doer. You’ll lead projects and be hands\-on in implementing solutions. Expect to lead meetings with internal stakeholders, document requirements, assess technical feasibility, consult and collaborate with SMEs, create technical documentation including technical diagrams, strategy manifestoes, user flows, agent prompts, and data flows.
- The owner of technical expertise and a deft interpersonal touch. You can go deep on MCP servers with an architect on Monday and the next day talk brand safety and creative concepting with a brand creative lead.
- An expert at balancing blue\-sky ideation with real\-world solutioning—and you know when to emphasize one versus the other.
- A top\-notch presenter.
- Flexible, adaptable, and fluid.
- 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.
- Proficient in using a range of tools \- including the Atlassian suite, Figma, and diagramming tools \- to effectively manage and support fast\-paced projects.
- Collaborative and detail\-oriented, with a proven ability to thrive in dynamic environments.
We’re looking for strong, impactful work experience, which typically includes:
- 7\+ years in technology roles, with experience supporting cross\-functional development teams in an agile environment.
- Bachelor’s degree (or equivalent) in Business or Technology related field preferred
- Hands\-on experience with generative AI, machine learning, or related technologies.
- Familiarity with MarTech platforms, tools, and the digital marketing or advertising technology landscape.
- Proven capability in system/business analysis and driving product development from ideation to launch.
- Excellent analytical, creative problem\-solving, and critical thinking skills.
- Adaptability to handle risk, change, and feedback while maintaining composure under pressure.
- Experience supporting product or application development, with exposure to agile/Scrum methodologies.
- Ability to troubleshoot, problem\-solve, and support technical delivery across multiple projects.
Preferred:
- Experience in a professional services, agency, or consulting environment.
- Experience presenting technical concepts to clients or stakeholders.
- Experience using no/low\-code platforms to create multi\-step agents/workflows.
Ready to grow your career and help shape the future of AI\-driven product innovation at Digitas? Apply today!
2026\-153086
Salary Context
This $152K-$172K range is below 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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($162K) sits 26% below the category median. Disclosed range: $152K to $172K.
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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