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 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.
- Author SDLC Standards and Engineering Operating Model artifacts: quality gates, PR/review standards, Definition of Done/Ready templates, CI/CD control specifications.
- Produce EA leadership artifacts: Enterprise Capability Maturity Map, Technology Demand and Decision Flow Dashboard, Application/Tool Portfolio Map, cross-lane integration maps, and an AI Readiness Scorecard.
- Operate as the primary technical liaison between the enterprise architecture function and service line teams, translating strategic architecture intent into actionable engineering guidance.
About You
- 8+ years in software architecture, solutions architecture, or enterprise architecture roles.
- Demonstrated experience designing and building AI/LLM-integrated systems: agent architectures, prompt engineering, RAG pipelines, tool-use patterns.
- Strong understanding of context engineering: structured prompt design, system prompt authoring, knowledge retrieval optimization, context window management.
- Experience with at least two of: MCP (Model Context Protocol), LangChain/LangGraph, Claude API, OpenAI API, or equivalent LLM orchestration frameworks.
- Proficiency in multiple programming languages (Python, TypeScript/Node.js required; others a plus).
- Proven track record authoring governance artifacts: ADRs, architecture standards, SDLC documentation, security review frameworks.
- Experience with enterprise platform ecosystems: Atlassian (Confluence, Jira), Azure, GitHub, Salesforce.
- Strong technical writing and documentation skills; able to produce executive-ready architecture briefs and technical specifications simultaneously.
Preferred Qualifications
- 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).
Pay Range: $150,000.00 – 180,000.00
The range shown represents a grouping of relevant ranges currently in use at Material. Actual range for this position may differ, depending on location and specific skillset required for the work itself.
