Senior Architect / AI Context Engineer

Material

. Design, author, and maintain MCP server architectures for knowledge and memory servers, platform API bridge servers, and event- or webhook-triggered agents .

Posted 10/10/2026full-timeUnited StatesSenior💰 $150,000 - $180,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in software architecture and enterprise governance, with a strong focus on AI/LLM-integrated systems, context engineering, and technical documentation. Proficient in developing reusable agent skills and governance artifacts while serving as a technical liaison across teams.

Highest-signal resume keywords

Software ArchitectureAI/LLM-Integrated SystemsContext EngineeringPython ProgrammingGovernance Artifacts Authoring

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software ArchitectureAI/LLM-Integrated SystemsContext EngineeringPython ProgrammingTypeScriptPrompt EngineeringGovernance DocumentationSDLC StandardsKnowledge Graph DesignOntology Modeling

Soft Skills

Technical WritingDocumentation Skills

Tools & Technologies

MCPLangChainClaude APIOpenAI APIAtlassianAzureGitHubSalesforce

Industry Keywords

Enterprise ArchitectureGovernance ModelsMarket ResearchConsultingInsights Analytics

Tech Stack

Tools & technologies

AzureJavaScriptNode.jsPythonSDLCTypeScript

About the role

Key responsibilities & impact

Design, author, and maintain MCP server architectures for knowledge and memory servers, platform API bridge servers, and event- or webhook-triggered agents
Build and curate reusable agent skills, system prompts, scoring rubrics, and harness configurations for governed agent archetypes
Author and publish enterprise governance documentation, including ADRs, security review checklists, tiered governance models, and architecture review templates
Design and implement an enterprise context engineering layer with repo-level context file standards, templates, and harness engineering patterns
Drive execution of a governed internal agent platform pilot, including agent archetypes, governed sessions, scoped tool access, and audit trail validation
Develop and maintain an accelerator solution map with reusable toolkit patterns, capability gap analysis, and accelerator specifications
Support Enterprise Knowledge Graph scoping, including ontology design, data catalog foundation architecture, and schema governance
Author SDLC Standards and Engineering Operating Model artifacts, including quality gates, PR/review standards, Definition of Done/Ready templates, and CI/CD control specifications
Produce EA leadership artifacts, including capability maturity maps, technology demand and decision flow dashboards, application/tool portfolio maps, integration maps, and AI readiness scorecards
Serve as the primary technical liaison between enterprise architecture and service line teams, translating strategic architecture intent into actionable engineering guidance

Requirements

What you’ll need

8+ years in software architecture, solutions architecture, or enterprise architecture roles
Demonstrated experience designing and building AI/LLM-integrated systems, including agent architectures, prompt engineering, RAG pipelines, and tool-use patterns
Strong understanding of context engineering, including structured prompt design, system prompt authoring, knowledge retrieval optimization, and context window management
Experience with at least two of MCP, LangChain/LangGraph, Claude API, OpenAI API, or equivalent LLM orchestration frameworks
Proficiency in Python and TypeScript/Node.js; additional programming languages are a plus
Proven track record authoring governance artifacts, including ADRs, architecture standards, SDLC documentation, and security review frameworks
Experience with Atlassian (Confluence, Jira), Azure, GitHub, and Salesforce
Strong technical writing and documentation skills, producing executive-ready architecture briefs and technical specifications
Hands-on experience with a major agentic AI ecosystem (preferred)
Background in market research, consulting, or insights/analytics industries (preferred)
Experience with knowledge graph design, ontology modeling, or semantic data architectures (preferred)
Familiarity with institutional memory and knowledge management platforms, particularly internal developer tooling that exposes organizational context to AI agents (preferred)
Experience with enterprise governance models such as TOGAF, Zachman, or custom tiered governance (preferred)

Benefits

Comp & perks

Learning and capability development opportunities
Work with exceptionally talented colleagues across the company, country, and world
Opportunity to contribute to leading-edge market offerings and global practices
Outcomes-focused work creating experiences, new value, and impact