Head AI Center of Excellence

Somani Technologies; Navi Mumbai; On-site; INR 9,000,000 - 14,000,000; Full time

Job summary

Jio-bp in Navi Mumbai is seeking a Head of AI CoE to own and drive enterprise AI transformation across data platforms, BI, Data Science, GenAI, and MLOps. You will lead the CoE leadership team, coordinate with partners, and own budget planning, optimization, and value realization while ensuring security and Responsible AI compliance.

The role requires 15-20+ years in data/AI leadership, a strong track record of building CoEs, and deep expertise in lakehouse, integration, and AI delivery.

Qualifications

• 15-20+ years in data/AI leadership with experience building and scaling CoEs or enterprise transformation programs.
• Proven track record leading multi-squad programs with measurable outcomes and adoption at scale.
• Strong understanding of modern data + AI delivery: lakehouse, integration, BI, ML, GenAI/RAG, and MLOps/LLMOps.
• Strong enterprise governance: Security/IRM, privacy, audit readiness, Responsible AI and controlled release discipline.
• Budget ownership with forecasting, FinOps, unit economics, and value-for-money decisioning.
• Proven partner/vendor management: multi-partner delivery, accountability, and executive stakeholder management.
• Depth in at least one pillar (Data Engineering / BI / AI-ML / GenAI / MLOps / Cloud Architecture) with AI/ML focus.
• Excellent leadership communication and stakeholder management across business, technology, governance and partners.
• Retail domain strength is must; Oil & Energy/Mobility is a strong plus.

Responsibilities

• Define the AI CoE vision, operating model, and multi-year roadmap aligned to 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.
• Build and lead end-to-end CoE teams across Data Engineering, 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.
• Oversee multi-squad execution: cadence, milestones, dependencies, RAID governance, and escalation closure.
• Drive enterprise release discipline: Dev/UAT/Prod readiness, handover, and sustained support model.
• Sponsor and govern enterprise data + AI platform architecture; ensure MLOps/LLMOps and observability standards.

Skills

Data leadership

CoE scaling

Program leadership

GenAI/LLMOps

Budget ownership

Vendor management

Enterprise governance

AI strategy

Stakeholder management

Education

B.Tech./B.E. or MBA

Job description

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).