About the company
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Executive Director, Agentic Lab and Architecture Lead based in United States.
Responsibilities
- Define enterprise architecture, reference patterns, technical standards, and engineering guardrails for secure, scalable, and governed agentic AI capabilities.
- Establish reusable architectural approaches covering agent orchestration, multi-agent systems, memory, RAG, tool and function calling, APIs, evaluation, observability, and enterprise integration.
- Lead the Agentic Lab innovation roadmap, creating an environment for rapid experimentation, technical validation, and disciplined incubation of high-potential AI technologies.
- Evaluate foundation models, agent frameworks, orchestration technologies, developer tools, and emerging approaches to determine their suitability for enterprise adoption.
- Develop reusable proof-of-concepts, accelerators, architectural blueprints, and reference implementations that accelerate delivery and reduce duplicated effort across AI initiatives.
- Drive adoption of shared technical assets and standards while establishing scalable patterns that support consistent implementation across enterprise AI solutions.
- Partner with AI engineering, platform engineering, product, architecture, security, data, technology, and business teams to transition validated prototypes into production-ready capabilities.
- Oversee the technical readiness and handoff of lab concepts into scaled implementations, including architecture reviews, documentation, risk assessments, and operating model transitions.
- Embed Responsible AI, security, privacy, accessibility, data governance, regulatory, and quality requirements into architectures and experimentation practices from the outset.
- Establish evaluation and monitoring standards covering agent reliability, explainability, performance, safety, human oversight, and measurable business value.
- Lead, coach, and develop architects, engineers, and other technical talent, promoting disciplined experimentation, engineering excellence, reuse, and continuous learning.
- Communicate architecture decisions, technical trade-offs, risks, investment requirements, progress, and strategic recommendations clearly to senior executives and cross-functional stakeholders.
Requirements
- Bachelor’s or advanced degree in Computer Science, Artificial Intelligence, Engineering, or another relevant technical discipline.
- 12+ years of experience designing enterprise software platforms, AI architectures, data and AI products, or cloud-native engineering capabilities, including significant technical leadership experience.
- Deep expertise in LLMs, agent frameworks, multi-agent orchestration, RAG, vector and graph databases, tool/function calling, memory, evaluation, observability, APIs, event-driven architectures, and cloud-native deployment.
- Proven ability to establish enterprise reference architectures, reusable patterns, technical standards, guardrails, and validated blueprints for secure and scalable AI deployment.
- Strong hands-on understanding of modern software engineering and AI delivery practices, including version control, CI/CD, testing, release readiness, model and prompt evaluation, telemetry, cost optimization, performance management, and operational support.
- Demonstrated ability to evaluate emerging technologies through technical scouting, laboratory experimentation, architecture review, production-readiness assessment, and transition into engineering roadmaps.
- Strong knowledge of Responsible AI, security, privacy, data governance, human oversight, access controls, prompt and model risks, auditability, and compliance requirements for enterprise AI.
- Experience building enterprise agentic AI platforms, developer ecosystems, or AI architecture practices in a regulated industry such as pharmaceuticals, healthcare, financial services, or another highly governed environment is preferred.
- Familiarity with technologies such as Microsoft Azure AI Foundry/OpenAI, Semantic Kernel, Copilot Studio, LangChain/LangGraph, MCP/A2A protocols, knowledge graphs, GraphRAG, and enterprise search architectures is advantageous.
- Demonstrated ability to influence senior technology and business leaders on architecture trade-offs, investment decisions, platform reuse, operational risk, and enterprise scaling strategies.
- Proven experience coaching principal engineers, architects, AI engineers, and product teams on reusable architecture patterns and disciplined innovation.
- Strong executive communication, stakeholder engagement, strategic t