VP, AI Solutions Architect

VP, AI Solutions Architect

The Role We're seeking a hands-on AI Solutions Architect at the Vice President level to join Merkle’s Office of the CTO. You will personally design, code, prototype, and deploy AI solutions that solve real business problems, while shaping our architecture, platform, and engineering standards as a founding member of a new AI Solutions team.

Reporting to the SVP, AI Solutions, you will combine the implementation depth of a senior individual contributor with the judgment and communication skills to advise enterprise clients. The VP title reflects the scope of your technical ownership and influence. Writing code, debugging systems, and shipping working software are core, ongoing responsibilities.

You should enjoy moving between business discovery, architecture decisions, and implementation. You’ll work directly with clients and a globally distributed team of AI specialists to turn ambiguous requirements into working prototypes, develop promising solutions for production, and improve them through evaluation and real-world feedback.

We welcome candidates from Staff or Principal Engineer, hands-on Technical Lead, founding engineer, and similar backgrounds. You should bring recent experience building AI systems yourself and a desire to remain deeply involved in delivery.

What You’ll Do

• Build and ship AI solutions

• Personally implement AI applications and agentic workflows, including application code, orchestration, APIs, data integrations, and deployment pipelines.

• Take solutions from technical discovery and rapid prototyping through production deployment, monitoring, and iteration, working with client and internal engineering teams.

• Build integrations between AI services and enterprise systems using APIs, event-driven patterns, and Model Context Protocol (MCP) where appropriate.

• Apply LLMs, retrieval-augmented generation (RAG), agent orchestration, and other AI techniques to concrete business needs, including customer service, marketing personalization, and analytics automation.

• Write tests, build evaluation datasets, implement observability, and debug failures. Use evidence to improve output quality, reliability, latency, and cost.

• Shape architecture through implementation

• Design and implement architectures that meet enterprise requirements for scalability, security, privacy, reliability, and performance.

• Make and explain practical tradeoffs across models, frameworks, cloud services, and build-versus-buy decisions.

• Turn successful implementations into reusable components, reference architectures, integration patterns, and delivery templates.

• Help evolve Merkle’s AI platform through direct engineering contributions and standards for orchestration, evaluation, deployment, monitoring, and responsible AI.

• Integrate relevant partner technologies across platforms such as Salesforce, Adobe, Microsoft, AWS, Google Cloud, Databricks, and Snowflake.

• Lead through technical delivery

• Run technical discovery with clients to identify valuable use cases, understand their systems and constraints, and define an achievable implementation approach.

• Build and demonstrate working prototypes with clients and internal teams; lead hands-on technical workshops and hackathons.

• Explain architecture decisions, implementation risks, and results to both executive and technical audiences.

• Mentor architects and engineers through pairing, code reviews, architecture reviews, and shared problem-solving.

• Partner with Technology Strategy, Data Science, Security, Cloud, and Practice teams to meet enterprise requirements and support deployment.

• Support Sales with technical solution design, working demos, and delivery estimates grounded in implementation experience.

• What You’ll Bring

• Required Qualifications

• 12+ years of progressive experience in software engineering, solution architecture, enterprise architecture, or technical consulting, including at least 3 years designing and delivering production generative AI/LLM systems.

• Recent, substantial hands-on coding experience, with proficiency in one or more languages such as Python, TypeScript/JavaScript, C#, or Java. You can independently implement and debug significant parts of a solution.

• Concrete examples of AI systems you personally built and shipped. You can explain your individual contribution, the architecture and code you owned, the deployment approach, and what you learned from production use.

• Practical experience building agentic systems with modern orchestration frameworks, such as LangGraph, Microsoft Agent Framework, OpenAI Agents SDK, Claude Agent SDK, or CrewAI.

• Experience applying LLM evaluation and observability practices, plus working knowledge of RAG and vector search patterns.

• Strong software engineering foundations across API design, data integration, cloud deployment, containers, automated testing, and CI/CD.

• Understanding of event-driven integration, MCP, and production monitoring, with the ability to diagnose issues across application and infrastructure boundaries.

• Ability to turn ambiguous business goals into working software and scalable, secure architectures, and communicate tradeoffs clearly to clients and colleagues.

• A desire to remain an active individual contributor while providing senior technical leadership.

• Preferred Qualifications

• Experience deploying solutions on managed cloud AI and agent platforms from Microsoft, AWS, or Google Cloud.

• Experience integrating AI with enterprise marketing, commerce, or data platforms such as Adobe Experience Cloud, Salesforce, Braze, Shopify, Marketplacer, Databricks, or Snowflake.

• Full-stack development experience spanning user interfaces, backend services, data storage, and infrastructure.

• Classical ML experience, including predictive modeling, PyTorch, or MLOps, alongside generative AI expertise.

• Experience establishing engineering practices, building technical communities, and mentoring senior engineers while remaining hands-on.

• Examples of technical initiative through work projects, independently launched products, open-source contributions, or technical writing. Public code and side projects are welcome but are not required.

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience. Relevant AI/ML, cloud, or enterprise architecture certifications are a plus.

• When describing your experience, highlight what you personally designed, implemented, and shipped, along with the outcome. Examples can come from your employment; please respect confidentiality and proprietary information.

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.

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 requir