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.

1. 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.

1. 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.
1. 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.

1. 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.

1. 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.

1. 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.

1. 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 (robust plus).

📌 Head AI Center of Excellence (Navi Mumbai)
🏢 Somani Technologies
📍 Navi Mumbai