Lumax - Head - Data & AI CoE in Gurgaon, भारत

1. CoE & Operating Model : - Design and operationalize the CoE : charter, federated model (central platform + BU pods), talent architecture, multiyear roadmap across all entities.- Own the Data & AI product portfolio : prioritized by business impact x feasibility, tracked with documented ROI for each BU.- Establish CoE KPIs : models in production, time-to-insight, cost avoidance, revenue impact, AI literacy index, and data trust score.- Introduce data product thinking : treat datasets as products with owners, SLAs, consumers, and version control, not as outputs of one-off projects.2. Data Platform & Architecture : - Group-level data Lakehouse : real-time OT ingestion from 42-plant MES/SCADA/PLCs; unified data mesh across BU domains.- SAP S/4HANA MDM, metadata cataloguing, data quality governance across all entities.- MLOps / LLMOps infrastructure : model registry, CI/CD for ML, drift monitoring, RAG architecture standards, hallucination guardrails for manufacturing-critical applications.- GenAI architecture decision : Azure OpenAI vs. open-weight LLMs (Llama/Mistral) vs. on-premise for IP-sensitive manufacturing data.- Self-serve analytics enablement : build the platform and semantic layer that allows BU business users to answer their own data questions without engineering dependency.3. Data Observability & Data Trust : - Own data observability as a first-class function not a monitoring afterthought. Establish Lumax's data observability programme covering the five pillars : freshness, volume, schema, distribution, and lineage across all pipelines and BU data products.- Define and enforce data SLAs and data contracts between producers and consumers; any dataset serving a plant decision or board dashboard must carry an explicit data contract.- Implement tooling for automated anomaly detection, root cause analysis, and mean-time-to-detect (MTTD) and mean-time-to-resolve (MTTR) measurement across data pipelines targeting industry-leading MTTD of minutes, not days.- Build a data trust score framework : every data asset in the Lumax ecosystem is assigned a trust score visible to all consumers, covering completeness, freshness, lineage depth, and SLA adherence.- Govern data lineage end-to-end : from MES sensor at plant floor to GMD dashboard; every transformation, join, and aggregation documented and auditable; critical for DPDP Act compliance and OEM/JV audit readiness.- Evaluate and recommend best-fit observability tooling : Monte Carlo.4. MLOps, LLMOps and AI Engineering : - End-to-end MLOps : feature stores, model versioning (MLflow), A/B testing, production SLAs by risk tier (plantsafety models : Tier 1 highest rigor; internal productivity tools : Tier 4 lightweight).- LLMOps : prompt governance, RAG standards, hallucination monitoring, human-in-the-loop for manufacturing-critical model outputs.- Model cards and AI audit trails for every production model covering training data provenance, performance benchmarks, known failure modes, and approved use contexts.5. Data Governance and Responsible AI : - Enterprise data governance framework : ownership, stewardship, lineage, data contracts, and data quality SLAs across all entities.- AI Ethics Charter : DPDP Act (India) + GDPR (JV data flows) compliance; AI risk tier classification Tier 1 - 4.- Data access control and privacy engineering : column-level security, dynamic data masking for PII, and purpose-bound data access aligned to DPDP Act 2023.- Experimentation culture : institutionalize fast-fail as learning; documented retrospectives, cross-BU re-use of findings; psychological safety is a formal leadership KPI, not a cultural aspiration.6. Talent, culture & AI literacy : - Build CoE team : Data Scientists, ML/AI Engineers, Data Engineers, MLOps Engineers, Data Observability Engineers, Analytics Translators, BI Developers + BU data liaisons.- Lumax Intelligence Brief : AI literacy roadmap for 10,000+ employees, differentiated by persona (plant operator -> C-suite).- CoE talent brand : MNC/Big Tech lateral hires; structured data career pathways.- Establish data engineering culture benchmarks adapted to Lumax's manufacturing-first context.7. Stakeholder Partnership and Value : - Trusted partner to all BU CEOs, 42 plant heads, 10 JV leads. Every AI initiative carries a business case and benefits realisation plan per BU.- Automotive value chain intelligence : build data products that serve not just Lumax's internal operations but generate insights across the OEM-Tier1-supplier value chain including OEM demand signals (Maruti, Honda, Tata, BMW India supply chain), JV performance analytics, and aftermarket channel intelligence.- OEM relationship data advantage : own the data strategy that makes Lumax the most analytically transparent and predictive Tier-1 partner for OEM customers.- External representation : industry bodies, automotive AI councils, CII/ACMA data forums, Tier-1 thought leadership. (ref:hirist.tech)