Job Title

About the CompanyWe are a next-generation AI consulting, strategy, and engineering practice focused on transforming enterprises into Agentic AI-powered organizations — intelligent, autonomous, and deeply data-driven. Data is the fuel of agentic AI. Without high-quality, mapped, accessible, governed data, no AI agent can perform reliably.About the RoleThe AI Data Strategist plays a critical role in shaping enterprise data readiness, ensuring the data foundations required for agentic AI workflows, reasoning, decisioning, and orchestration are in place. You are the bridge between business processes, data ecosystems, AI models, and agentic systems.ResponsibilitiesOwn data readiness and enterprise data mapping for all agentic AI solutions. Ensure the right data is available, accessible, trustworthy, and structured to power reasoning, RAG, agent workflows, and AI decisioning.Lead Enterprise Data Readiness AssessmentEvaluate current-state data maturity acrossCompletenessAccuracyTimelinessLineageOwnershipAccessibilityComplianceAssess readiness of structured, semi-structured, and unstructured data.Identify data gaps that block AI agent performance (missed fields, inconsistent labels, missing documents, poor metadata, etc.).Evaluate data availability across core platforms (CRM, ERP, HRMS, core banking, SCM, procurement, etc.).Drive Process-to-Data MappingWork with Functional Design Leads to translate workflows into data needs.Identify what data each agent requires for:ContextReasoningRAG recallDecision logicException handlingBuild Data Dependency Maps linking:Process → Tasks → Data Elements → Source SystemsCreate Data Blueprints for each agent or workflow.Define Agentic AI Data Architecture RequirementsWork with Technical Pod Lead and Data Engineering teams to:Determine required ingestion pipelinesIdentify vectorization opportunities for RAGDefine semantic layers for reasoningRecommend transformations for agentic workflowsDefine context windows, embeddings, retrieval needs, memory systems, and data enrichment requirements.Partner with Data Engineering & Platform TeamsTranslate data requirements into ingestion, transformation, and storage specifications.Validate feasibility of data access and integration.Support creation of pipelines for structured data, documents, logs, and knowledge repositories.Align with MLOps/LLMOps for:Data refresh cyclesIndexingIncremental updatesPerform Data Gap & Quality AnalysisConduct data profiling and metadata discovery.Identify issues affecting agent accuracy (duplicates, missing values, inconsistent formats, non-standard taxonomies).Recommend data cleansing, enrichment, remediation, or restructuring.Work with data governance teams to define ownership and ongoing maintenance.Ensure Compliance & Responsible Data PracticesValidate data access risk, privacy constraints, and PII exposure.Collaborate with CoE governance for ethical AI usage.Define secure access patterns and masking requirements for AI workloads.Support Pod Teams During BuildProvide ongoing data clarifications for FDEs and technical teams.Validate that prototypes and agents are consuming the correct data.Troubleshoot issues related to data retrieval, latency, and context accuracy.Ensure data quality supports performance KPIs.Contribute to Practice IP & Data FrameworksCreate reusable:Data readiness templatesData maps and catalogsData requirement questionnairesSemantic mapping guidesRAG data preparation patternsHelp institutionalize Agentic AI Data Methodology across the practice. Qualifications8–15 years in data strategy, data architecture, analytics consulting, or enterprise data transformation.Strong background in data discovery, data quality, data modeling, metadata management, and data governance.Experience in digital transformation or AI/ML-focused programs.Exposure to industry data models (Banking, Retail, Manufacturing, Supply Chain, HR, etc.).Prior consulting experience strongly preferred. Required SkillsStructured problem-solving and analytical thinkingDeep understanding of enterprise systems and data sourcesAbility to simplify complex data ecosystemsStrong documentation and visualization capabilityGood knowledge of LLM/RAG patterns and data requirementsAbility to collaborate across business, tech, and governance stakeholdersUnderstanding of data privacy, security, and responsible AI compliance Preferred SkillsCertifications in Data Governance, Data Strategy, Cloud Data Platforms, or Data Architecture preferred.