Head AI Center of Excellence

Role Overview

The Head of AI CoE will own and drive Jio-bps enterprise AI transformation—covering Data

Platforms, Data Engineering & Integration, BI, Data Science (incl. OR), GenAI,

MLOps/LLMOps, and AI/Cloud Architecture. Reporting to the Chief Digital Officer (CDO),

this is a senior leadership role responsible for defining AI strategy, building and leading high-

performing teams, and delivering measurable business outcomes across functions—while

ensuring enterprise-grade Security/IRM compliance, Responsible AI, and operational

excellence.

You will lead the CoE leadership team (Chief AI Architect, Chief Data Scientist, Chief AI Product

Manager, and their teams), coordinate a multi-partner ecosystem, and own budget planning,

optimization, and value realization across platform and delivery.

Key Responsibilities

1) AI Strategy, Roadmap & Value Realization

• Define the AI CoE vision, operating model, and multi-year roadmap aligned to the Digital

strategy and business priorities.

• Own portfolio governance: intake, prioritization, sequencing, and value tracking (ROI,

productivity, CX improvements, risk reduction).

• Establish and track outcome KPIs; drive adoption and measurable value realization

through structured change management.

2) Organization Building & Leadership

• Build and lead end-to-end CoE teams across Data Engineering & Integration, BI, Data

Science, GenAI, MLOps/LLMOps, AI Architecture, and Cloud Architecture.

• Define ways of working, delivery standards, performance goals, and talent strategy

(hiring, coaching, succession).

• Create a high-ownership culture with delivery rigor, documentation discipline, and

continuous improvement.

3) Program Execution & Delivery Governance

• Oversee multi-squad execution: delivery cadence, milestones, dependencies, RAID

governance, and escalation closure.

• Ensure smooth collaboration with business SPOCs, operations teams, and central

technology teams for UAT, rollout, and scale.

• Drive enterprise release discipline: DevUATProd readiness, operational handover,

hypercare, and sustained support model.

4) Platform, Architecture & Engineering Excellence

• Sponsor and govern the enterprise data + AI platform architecture: lakehouse

foundations, integration patterns, model serving patterns, and GenAI/RAG standards.

• Ensure robust MLOps/LLMOps and observability standards: drift/quality/latency, safety

signals, token/cost metrics, SLOs, runbooks, incident processes.

• Drive standardization and reuse of patterns/templates to accelerate delivery and reduce

operational risk.

5) Budget Management, FinOps & Cost Optimization

• Own the AI CoE budget across platform and delivery (CapEx/OpEx as applicable)

including planning, forecasting, and governance.

• Drive cost optimization across compute, storage, licensing, and partner commercials;

establish usage guardrails and cost-to-value benchmarks.

• Implement FinOps discipline: unit economics (cost per model/run/interaction), showback

inputs, and periodic optimization reviews.

• Ensure delivery scope, partner capacity, and resourcing plans remain aligned to budget

envelopes without compromising critical outcomes.

6) Governance, Security/IRM & Responsible AI

• Own AI governance: Responsible AI, privacy-by-design, auditability, approval workflows,

and compliance alignment with Security/IRM.

• Ensure enterprise controls for GenAI: guardrails, evaluation evidence, access controls,

sensitive data handling, and safe rollout practices.

• Drive risk management across model lifecycle, vendor dependencies, and production

support.

7) Partner Ecosystem & Vendor Management

• Own the multi-partner delivery model: partner strategy, selection inputs, squad capacity

planning, delivery accountability, and quality gates.

• Manage commercials and performance governance: SLAs, milestones, delivery metrics,

and escalation paths.

• Build strong alignment with RIL central teams and ecosystem partners to adopt common

playbooks and accelerate delivery.

8) Stakeholder Management, Networking & AI Evangelism

• Act as the executive face of AI at Jio-bp: provide regular updates to leadership forums

and steercos.

• Build strong networks across business units, RIL ecosystem, and relevant external

forums to bring best practices, talent pipelines, and innovation.

• Lead capability building: leadership enablement, AI SPOC engagement, and

playbooks/templates to scale adoption.

Must-Have Skills & Experience

• B.Tech./B.E. and/or Master’s in a relevant field (CS/IT/AI/Statistics/Math/Economics) or MBA (or

equivalent).

• 15–20+ years across Data/AI/Analytics leadership with experience building and scaling

CoEs or enterprise transformation programs.

• Proven track record of leading multi-squad programs with measurable outcomes and

adoption at scale.

• Strong understanding of modern data + AI delivery: lakehouse, integration, BI, ML,

GenAI/RAG fundamentals, and MLOps/LLMOps.

• Strong enterprise governance experience: Security/IRM alignment, privacy, audit

readiness, Responsible AI, and controlled release discipline.

• Strong budget ownership and cost optimization experience: forecasting, FinOps

discipline, unit economics, and value-for-money decisioning.

• Proven partner/vendor management: multi-partner delivery, accountability models, and

executive stakeholder management.

• In-depth expertise in at least one core pillar (Data Engineering / BI / AI-ML / GenAI /

MLOps / Cloud Architecture), with strong depth in AI/ML preferred.

• Excellent leadership communication, influence, and stakeholder management across

business, technology, governance, and partners.

• Domain strength in Retail (Must); Oil & Energy/Mobility (strong plus).