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About This Role
Job Description:
The Role
We are seeking a highly technical and innovative Vice President, AI Solutions Architect to join our Office of the CTO. You will be a founding member of a new AI Solutions team, with direct influence on how the practice, its platform, and its standards take shape. This role blends hands\-on technical architecture, strategic platform development, and client\-facing leadership. As VP of AI Solutions Architecture, you will be the technical authority who translates cutting\-edge AI capabilities into enterprise\-ready solutions that deliver measurable business value.
Reporting to the SVP, AI Solutions, you will lead the design and implementation of complex AI and agentic architectures, drive our platform integration strategy, and serve as the senior technical voice on our most strategic client engagements. The role demands deep technical expertise combined with the ability to orchestrate multi\-faceted solutions that draw on our portfolio of AI technologies, cloud partnerships, and emerging capabilities. You will work with a globally distributed team of AI specialists to build prototypes, lead technical hackathons, and pioneer solutions that position Merkle at the forefront of enterprise AI adoption.
This is an opportunity to shape how Fortune 500 companies implement AI at scale, while directly influencing Merkle’s AI platform evolution and technical strategy.
WhatYou’llDo
Architecture \& Solution Design
- Design end\-to\-end AI and agentic solutions that meet enterprise requirements for scalability, security, reliability, and performance.
- Create reference architectures, patterns, and blueprints for common use cases (e.g., customer service, marketing personalization, analytics automation).
- Define integration approaches, APIs, and Model Context Protocol (MCP) connectivity between AI services and platforms such as Salesforce, Adobe, Microsoft, AWS, Google Cloud, Databricks, and Snowflake.
- Apply a range of AI techniques, including large language models (LLMs), multi\-agent systems, retrieval\-augmented generation (RAG), computer vision, and predictive analytics, to solve real business problems.
- Embed governance, privacy, and responsible AI requirements into solution designs from the outset.
Platform Strategy \& Reuse
- Guide the evolution of Merkle’s AI platform, emphasizing modularity, reusability, observability, and security.
- Integrate third\-party and partner technologies where appropriate; build reusable components, accelerators, and templates to reduce delivery time.
- Establish standards for agent orchestration, evaluation and quality gates, data pipelines, monitoring, and responsible AI.
Innovation \& Technical Leadership
- Lead hands\-on prototyping, proofs of concept, and technical hackathons with clients and internal teams.
- Conduct architectural reviews and deep dives for complex programs; mentor solution architects and AI engineers.
- Translate emerging research and platform roadmaps into practical enterprise applications.
Client Engagement \& Delivery
- Advise clients on technology selection, architecture decisions, and implementation strategies.
- Run technical discovery to assess current state, identify AI opportunities, and de\-risk delivery.
- Communicate clearly with both executive and technical audiences; support critical deployment phases.
Cross\-Functional Collaboration
- Partner with Technology Strategy, Data Science, Security, Cloud, and Practice teams to align AI solutions with enterprise roadmaps and governance.
- Support Sales with technical solutioning, demos, and estimates.
WhatYou’llBring
Required Qualifications
- 12\+ years of progressive experience in solution architecture, enterprise architecture, software engineering, or technical consulting, including at least 3 years designing and delivering production generative AI/LLM systems.
- Hands\-on experience building agentic systems with modern orchestration frameworks (e.g., LangGraph, Microsoft Agent Framework, OpenAI Agents SDK, Claude Agent SDK, CrewAI).
- Working knowledge of LLM evaluation and observability practices and tooling (e.g., LangSmith, Arize, Braintrust), plus RAG and vector search patterns.
- Strong background in cloud\-native architectures and modern engineering practices (e.g., containers, microservices, CI/CD, DevOps).
- Ability to translate business goals into scalable, secure architectures and to communicate trade\-offs to diverse stakeholders.
- Proficiency in one or more programming languages (e.g., Python, Java, C\#, TypeScript/JavaScript).
- Understanding of API design, event\-driven integration, MCP, and production monitoring/observability.
Preferred Qualifications
- Experience with managed cloud AI and agent platforms (e.g., Microsoft Foundry (formerly Azure AI Foundry), AWS Bedrock and AgentCore, Google Gemini Enterprise/Vertex AI).
- Platform expertise with Adobe Experience Cloud (AEM, AEP, GenStudio, AEP Agent Orchestrator), Salesforce (Agentforce, Data Cloud), Braze, Shopify, Marketplacer, or similar.
- Classical ML depth (e.g., PyTorch, predictive modeling, MLOps) alongside generative AI experience.
- Experience building technical practices and communities of practice and mentoring senior technical talent.
- Record of thought leadership (publishing, speaking, open\-source contributions).
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.
- Relevant certifications in AI/ML, cloud, or enterprise architecture are a plus.
Location
This is a full\-time position based in the United States with flexible work arrangements.
Hybrid Options (3 days in office):
- New York, NY
- Chicago, IL
- Detroit, MI
Remote: US\-based remote candidates welcome to apply
Travel: 30\-40% travel to client sites and Merkle offices required
Additional Information
The annual base salary range for this position is $163,000 \- $263,062\. 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.
Location:
New YorkBrand:
MerkleTime 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: $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 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 $218,750 based on 3,817 positions with disclosed compensation. Disclosed range: $163K to $263K.
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.
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 $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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