AI Architect

Role overview The AI Architect leads the strategy, architecture, and governance of enterprise AI, Generative AI, and Agentic AI initiatives. The role spans platform design, intelligent agent ecosystems, knowledge architectures, and governance frameworks that enable secure, business-aligned AI transformation at scale. It is a senior, cross-functional leadership position partnering with product, security, data, and engineering stakeholders.

Responsibilities - Define enterprise AI strategy, architecture standards, reference architectures, and forward-looking roadmaps. - Architect and govern AI platforms built on Microsoft AI Foundry, Amazon Bedrock, and Google Vertex AI. - Design Agentic AI solutions including autonomous agents, multi-agent orchestration, memory frameworks, and human-in-the-loop workflows. - Build enterprise knowledge architectures using knowledge graphs, ontologies, semantic layers, metadata management, and agentic retrieval. - Architect retrieval-augmented generation (RAG) solutions leveraging vector databases, graph databases, hybrid search, and enterprise knowledge platforms. - Establish AI and agent governance frameworks covering security, compliance, risk, responsible AI, and policy enforcement, plus observability, tracing, evaluation, and performance measurement. - Provide technical leadership, mentoring, and best practices for LLMOps, MLOps, CI/CD, and cloud-native operations, while evaluating emerging AI technologies and standards.

Requirements - 15+ years in enterprise architecture, software engineering, or cloud platforms. - 7+ years designing and delivering AI/ML, Generative AI, or Agentic AI solutions. - Deep expertise with Microsoft AI Foundry, Amazon Bedrock, Google Vertex AI, large language models, RAG, knowledge graphs, and AI governance. - Strong knowledge of Azure, AWS, and GCP, including enterprise integration, security, and scalable distributed systems. - Proven leadership influencing executive stakeholders and leading cross-functional teams. - Familiarity with interoperability standards such as MCP, A2A, and OpenAPI.

Nice to have - Master's degree in Computer Science, AI, Data Science, or a related field. - Cloud and AI certifications from Microsoft, AWS, or Google Cloud. - Experience leading enterprise AI transformation programs in large global organizations.

Benefits and work setup - Industry-leading benefits supporting holistic health and wellbeing. - Flexible work arrangements, with specifics depending on role and location. - Culture emphasizing inclusion, merit-based advancement, and equal opportunity employment.