Senior Architect / AI Context Engineer
- Compensation
- Not disclosed
- Location
- New York
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Architecture
- Posted
Skills
Nice to have
- Hands-on experience with a major agentic AI ecosystem: model providers' agent runtimes, skill and tool definitions, MCP servers, and managed or hosted agent platforms.
- Background in market research, consulting, or insights/analytics industries.
- Experience with knowledge graph design, ontology modeling, or semantic data architectures.
- Familiarity with institutional memory and knowledge management platforms, particularly internal developer tooling that exposes organizational context to AI agents.
- A grounded perspective on AI adoption: recognizing that velocity bottlenecks in AI-enabled delivery are typically process habits and organizational culture rather than tooling capability.
- Experience with enterprise governance models (TOGAF, Zachman, or custom tiered governance).
Full description
Senior Architect / AI Context Engineer
This role is to be based near one of our offices in New York City, Austin, or Los Angeles. (Hybrid)
About Us
We drive intelligent growth for ambitious businesses and leading brands.
Customer understanding is more potent and drives greater value when insights go to work directly within marketing and experience priorities. To give our clients that advantage, we put an insights-driven operating system at the core of our design, go-to-market, and digital experience solutions.
Material clients make smarter growth choices because they can see clearly how and when to take the shots that count. Together, we connect consumer intelligence with customer demand generation to build lasting, profitable relationships.
About the Senior Architect / AI Context Engineer Role
The Senior Architect / AI Context Engineer is a strategic generalist who operates at the intersection of enterprise architecture, AI operationalization, and context engineering. This role is responsible for authoring governance artifacts, designing agent and tool-server architectures, building reusable accelerator patterns, curating agent skills and prompt harnesses, and driving the AI-native design agenda across the organization. This is the role that bridges the gap between strategic architecture decisions and hands-on AI implementation.
What You'll Do
- Design, author, and maintain MCP (Model Context Protocol) server architectures across recurring patterns: knowledge and memory servers, platform API bridge servers, and event- or webhook-triggered agents, following established AI-native design principles.
- Build and curate reusable agent skills, system prompts, scoring rubrics, and harness configurations for a library of governed agent archetypes.
- Author and publish enterprise governance documentation: Architecture Decision Records (ADRs), security review checklists, tiered governance model documentation, and architecture review templates for the enterprise technology intake and approval pipeline.
- Design and implement an enterprise context engineering layer: repo-level context file standards (AGENTS.md / CLAUDE.md conventions), templates, and harness engineering patterns.
- Drive execution of a governed internal agent platform pilot: author agent archetypes, configure governed sessions with scoped tool access, and validate end-to-end audit trail flows on a managed agent runtime.
- Develop and maintain an accelerator solution map: reusable toolkit pattern definitions, capability gap analysis, and accelerator specifications spanning all service lines.
- Support Enterprise Knowledge Graph scoping: ontology design, data catalog foundation architecture, and schema governance, in collaboration with data science and commercial strategy teams.