VP, AI Solutions Architect

Technology

New York

About the job

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.

What You’ll Do

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.

What You’ll Bring

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 …