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About This Role
Job Description:
Location: New York, NY
Work Arrangement: Hybrid, 3 days in office
About the Role
Dentsu is seeking a Senior Vice President of Agentic Commerce Solutions to lead the development of next generation commerce technology and AI powered solutions across the enterprise.
This executive will report directly to the Head of Commerce for Dentsu North America and lead the development of agentic technology for dentsu.commerce within dentsu.connect, Dentsu’s enterprise technology platform. They will build the technical integrations, strategic partnerships and intelligent workflows that connect commerce intelligence, planning, activation and measurement.
Central to this role is advancing agentic commerce capabilities through Model Context Protocol (MCP), APIs and emerging AI architectures. Working in partnership with the EVP of Retail Media, the SVP will help shape the strategic product roadmap while owning the technical architecture, hands on development and delivery of dentsu.commerce.
The ideal candidate combines deep commerce expertise with strong software engineering experience and the ability to build, prototype and scale AI powered solutions. This leader must be equally comfortable defining technical strategy, writing code alongside engineering teams and translating emerging technologies into enterprise solutions that drive operational efficiency and measurable client growth.
Key Responsibilities
Product Development and Technology Architecture
- Partner with the Head of Commerce and EVP of Retail Media to establish the strategic product roadmap and development priorities for dentsu.commerce.
- Own the technical architecture and define capabilities connecting commerce intelligence, audience insights, planning, activation and measurement.
- Lead the development and delivery of dentsu.commerce within dentsu.connect, ensuring alignment with enterprise technology standards.
- Work directly with engineering teams to design, prototype, develop and test technical solutions, contributing hands on coding and technical problem solving where needed.
- Translate business requirements into technical specifications, development plans and scalable software solutions.
- Own technical execution plans, development milestones and delivery against defined business outcomes.
Agentic Commerce and MCP Integrations
- Architect and build MCP based connections, APIs and integrations across Dentsu’s commerce technology ecosystem.
- Develop and prototype AI agents and automated workflows that connect commerce data and intelligence directly to planning, activation, optimization and measurement.
- Partner with technology providers in the commerce ecosystem to enable secure, scalable agentic capabilities.
- Establish technical frameworks for agent orchestration, platform connectivity, governance and responsible AI deployment.
- Evaluate code, troubleshoot integration challenges and guide engineering teams in developing production ready agentic solutions.
- Identify opportunities to automate manual processes and accelerate commerce decision making.
Strategic Technology Partnerships
- Build senior relationships with commerce technology partners to influence integration roadmaps and identify opportunities for joint innovation.
- Evaluate emerging AI technologies, commerce platforms and integration frameworks through technical assessments, proof of concepts and hands on experimentation.
- Collaborate with internal technology and commercial stakeholders on strategic product partnerships and investment priorities.
- Identify opportunities to leverage partner capabilities to differentiate Dentsu’s commerce solutions.
Commercialization and Enterprise Growth
- Translate technical capabilities into differentiated commerce products that support enterprise growth.
- Partner with Commerce leadership to support product commercialization, adoption and strategic new business opportunities.
- Develop business cases for technology investments, including revenue opportunities, operational efficiencies and scalability.
- Establish frameworks to deploy and scale commerce solutions across clients, retailers and markets.
Qualifications and Experience
- 10+ years of experience in commerce technology, software engineering, digital product development, marketing technology or enterprise technology solutions.
- Proven experience developing and scaling technology products within an agency, technology company or enterprise environment.
- Strong hands on software development experience, ideally with Python, JavaScript or TypeScript, including experience building APIs, integrations and AI powered applications.
- Demonstrated ability to write, review and troubleshoot code and develop working prototypes independently.
- Strong understanding of the commerce and retail media technology ecosystem.
- Demonstrated experience architecting and implementing API integrations, platform connectivity and strategic technology partnerships.
- Practical experience with Model Context Protocol (MCP), large language models, agentic AI architectures and AI workflow orchestration.
- Experience translating technical capabilities into commercial products and scalable enterprise solutions.
- Ability to collaborate effectively with engineering, product, data science and commercial leadership teams.
- Strong understanding of enterprise technology governance, data privacy and responsible AI principles.
- Excellent executive communication, strategic thinking and stakeholder management skills.
- Agency experience supporting enterprise commerce or retail media clients preferred.
Additional Information
The annual base salary range for this position is $163,000 - $263,000. Placement within the salary range is based on a variety of factors, including relevant experience, knowledge, skills, and other factors permitted by law. Additionally, this position is eligible for discretionary incentive compensation.
Benefits available with this position include:
- Medical, vision, and dental insurance
- Life insurance
- Short-term and long-term disability insurance
- 401k
- Flexible paid time off
- At least 15 paid holidays per year
- Paid sick and safe leave
- Paid parental leave
Dentsu also complies with applicable state and local laws regarding employee leave benefits, including, but not limited to providing time off pursuant to the Colorado Healthy Families and Workplaces Act, in accordance with its plans and policies. For further details regarding Dentsu benefits, please visit www.dentsubenefitsplus.com.
To begin the application process, please click on the “Apply” button at the top of this job post. Applications will be reviewed on an ongoing basis, and qualified candidates will be contacted for next steps.
At dentsu, we believe great work happens when we’re connected. Our way of working combines flexibility with in-person collaboration to spark ideas and strengthen our teams. Employees who live within a commutable distance of one of our hub offices, currently located in Chicago, metro Detroit, Los Angeles, and New York City, are required and expected to work from the office three days per week (two days per week for employees based in Los Angeles). Dentsu may designate other Hub offices at any time. Those who live outside a commutable range may be designated as remote, depending on the role and business needs. Regardless of your work location, we expect our employees to be flexible to meet the needs of our Company and clients, which may include attendance in an office.
Dentsu is committed to providing equal employment opportunities to all applicants and employees. We do this without regard to race, color, national origin, sex , sexual orientation, gender identity, age, pregnancy, childbirth or related medical conditions, ancestry, physical or mental disability, marital status, political affiliation, religious practices and observances, citizenship status, genetic information, veteran status, or any other basis protected under applicable federal, state, or local law.
Dentsu is committed to providing reasonable accommodation to, among others, individuals with disabilities and disabled veterans. If you need an accommodation because of a disability to search and apply for a career opportunity with us, please send an e-mail to [email protected] by clicking on the link to let us know the nature of your accommodation request and your contact information. We are here to support you.
Location:
New YorkBrand:
Dentsu MediaTime Type:
Full timeContract Type:
Permanent Dentsu is committed to providing equal employment opportunities to all applicants and employees. We do this without regard to race, color, national origin, sex , sexual orientation, gender identity, age, pregnancy, childbirth or related medical conditions, ancestry, physical or mental disability, marital status, political affiliation, religious practices and observances, citizenship status, genetic information, veteran status, or any other basis protected under applicable federal, state, or local law.
Dentsu is committed to providing reasonable accommodation to, among others, individuals with disabilities and disabled veterans. If you need an accommodation because of a disability to search and apply for a career opportunity with us, please send an e-mail to [email protected] by clicking on the link to let us know the nature of your accommodation request and your contact information. We are here to support you.
Salary Context
This $163K-$263K range is above the median 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 Dentsu, 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. Disclosed range: $163K to $263K.
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.
Dentsu AI Hiring
Dentsu has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $263K - $263K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 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 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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