Job Description :

Required Information Details :

1. Role : Delivery Leader - AI & Data

2. Required Technical Skill Set : AI, Data Engineering & Analytics Program Delivery Management

Desired Competencies (Technical / Behavioral Competency) :

Must-Have :

- End-to-end AI & Data program delivery management across full lifecycle (Strategy, Design, Build, Deploy, Scale)

- Strong expertise across Data Engineering, Data Warehousing, Analytics, ML/AI model deployment, and GenAI solutions

- Data platform governance data architecture oversight, data quality frameworks, and MDM/data governance strategy

- AI program management model lifecycle governance, MLOps pipelines, responsible AI & ethics compliance

- P&L ownership, revenue accountability, and financial management of AI & Data engagements

- Resource management, pyramid management, and onboarding execution for diverse AI/Data talent

- Risk & issue management, escalation handling, and data privacy/regulatory compliance (GDPR, DPDP)

- Stakeholder, vendor & partner management in complex multi-domain data delivery environments

Good-to-Have :

- Excellent communication and client relationship management skills

- Hands-on experience with platforms such as Databricks, Snowflake, Azure OpenAI, AWS SageMaker, or Google Vertex AI

- Familiarity with GenAI use case industrialization and enterprise AI adoption frameworks

- Certifications : Google Professional Data Engineer, AWS Certified ML Specialty, Azure AI Engineer, or equivalent

- Analytical mindset, problem-solving aptitude, and strong business acumen

Responsibility of / Expectations from the Role :

A strong AI & Data Delivery Leader should be able to produce/own the following outputs :

1. AI & Data Delivery Plan + detailed Program Governance & Data Operating Model Framework

2. BCP + data risk/mitigation plan including data privacy, security, and regulatory compliance

3. Acceptance criteria + data quality assessments/model validation reports/sign-offs

4. Data migration & ingestion strategy + platform cutover plan + go-live readiness checklist

5. AI/ML model deployment plan + MLOps framework + handover to production operations

6. Weekly governance pack (RAID, KPIs, delivery status, model performance metrics)

7. Stakeholder communication plan and executive AI & Data transformation dashboards

8. Financial tracking, forecasting, value realization reporting, and P&L management