Lead the architecture and delivery of enterprise generative AI solutions across document processing, language, vision, and agentic systems. Design Azure-based pipelines, RAG workflows, multi-agent solutions, integrations, and human-in-the-loop processes. Partner with stakeholders, lead engineering teams, and establish MLOps, governance, security, cost optimization, and responsible AI practices.
As an AI Architect you will own the end-to-end architecture, design and lead delivery of enterprise-grade AI solutions across generative AI domains such as document processing, language, vision, and agentic (LLM-based) systems on cloud platforms preferably Azure. You will partner with customer stakeholders to translate business goals into robust, secure, and cost effective architectures , lead engineering teams and establish MLOps, governance and responsible-AI practices. The role requires deep hands-on experience with Azure AI services (and complementary cloud technologies), practical knowledge of document processing use-cases, and expertise building RAG, LLM verification, and multi-agent solutions. Core responsibilities: 1. Solution Architecture & Design • Design end-to-end pipelines: ingestion, pre-processing, OCR/layout analysis, extraction, normalization, reconciliation, validation, and human-in-the-loop. • Translate business problems (document extraction, contract analytics, claims processing, knowledge bases, chat assistants) into measurable ML objectives and architecture blueprints. • Facilitate stakeholder workshops (Jira/Confluence/Miro) to capture success criteria, SLAs and compliance requirements. • Architect hybrid solutions combining Azure Cognitive APIs, custom ML models, Azure OpenAI/LLMs and RAG to balance accuracy, latency and cost. • Define API/integration patterns (REST), event-driven messaging and connectors to Kafka systems. 2. Agentic & Generative AI Design • Design RAG workflows with embeddings and vector search for source-cited responses and hallucination mitigation. • Design and advise on agentic AI frameworks (multi-agent roles, tool-invocation patterns, context/state management) for autonomous or semi-autonomous assistants. • Specify MCP/server orchestration approaches (stateful context, plugin/tool integrations, secure communications).
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