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Metro Mumbai and attractive AI lead role, but niche MLOps/document-intelligence reduces generalist competition.
Demands specialized NLP, document-intelligence and MLOps expertise, limiting cross-industry transferability.
Requires deep ML/LLM, MLOps, and platform leadership, creating strict technical and domain filters.
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Lead design and governance of enterprise-scale AI architecture focused on document intelligence, indexing, and semantic search solutions.
Drive adoption of MLOps standards, pipelines, and best practices across multiple teams to ensure scalable, secure, and compliant AI platforms.
Provide technical leadership and align AI initiatives with business goals across the organization.
Strong expertise in NLP, information retrieval, document processing, and OCR technologies.
Proven experience designing and leading large-scale AI platforms with applied LLM systems.
Hands-on knowledge of MLOps tools and practices (e.g., MLflow, Azure ML, Kubeflow) and CI/CD for ML using Git-based pipelines.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in architecting enterprise AI with focus on document ingestion, classification, and vector search technologies.
Able to lead cross-team technical initiatives and define AI standards and governance.
Familiar with AI solution security, compliance, and responsible AI principles at an organizational level.