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Niche ML/LLM and document-intelligence specialization reduces candidate pool despite Mumbai metro location.
Highly specialized ML, LLM, and document-intelligence skills limit cross-industry transferability.
Requires deep ML, LLM, MLOps, and platform leadership — strong mandatory technical filters.
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Lead design and implementation of document intelligence and indexing AI solutions including OCR, NLP, embeddings, and vector search.
Define AI architecture, design standards, governance models, and enforce MLOps standards and best practices across teams.
Provide technical leadership across multiple teams to drive enterprise-scale AI initiatives aligned with business goals while ensuring Responsible AI, security, and compliance.
Strong expertise in NLP, information retrieval, document processing, and AI/ML with applied LLM systems.
Proven experience designing and leading large-scale AI platforms and enterprise AI initiatives.
Hands-on experience with OCR, document parsing frameworks, document indexing, chunking strategies, search technologies, and vector databases (e.g., Azure AI Search, OpenSearch).
Experience with MLOps tools and practices including CI/CD for ML using Git-based pipelines, MLflow, Azure ML, or Kubeflow.
Work Experience Required: Not explicitly mentioned in the JD.
Location Requirement: Mumbai, India.
Experienced in leading cross-functional AI teams with accountability for architecture, governance, and enterprise-scale AI platform delivery.
Strong operational and technical focus on AI platform scalability, security, and compliance, especially in document-centric AI use cases.
Familiar with MLOps standards enforcement and partnership with leadership to align AI strategy with business objectives.