- Own the enterprise AI roadmap, aligning AI investments to strategic priorities and quantified business value.
- Build and maintain a prioritized pipeline of AI/GenAI use cases across acquisition, engagement, risk, collections, service, and cost efficiency.
- Partner with the Transformation team to sequence interventions by impact, feasibility, and readiness, ensuring scarce capacity is directed to the highest-value opportunities.
- Work jointly with the Data Infrastructure and AI Infrastructure teams to shape data pipelines, model platforms, and deployment architecture required for each use case.
- Ensure business intent is carried through design, build, and deployment, resolving trade-offs between ambition, feasibility, and time-to-value.
- Drive timely and in-full (OTIF) implementation of prioritized AI initiatives through disciplined planning, milestone tracking, and issue resolution.
- Establish delivery cadence, dependency management, and escalation mechanisms in partnership with the Transformation PMO.
- Manage cross-functional risks, remove blockers, and hold owners accountable for committed timelines and outcomes.
- Collaborate with the Data Infrastructure team to ensure availability, quality, lineage, and governance of the data assets that power AI models.
- Work with the AI Infrastructure team on model platforms, ML Ops, deployment pipelines, monitoring, and scalability of production AI.
- Ensure AI solutions are engineered for reliability, reusability, and enterprise scale rather than one-off pilots.
- Define baselines, benefit estimates, KPI targets, and measurement methodologies for every AI intervention.
- Track realized impact on customer acquisition, CLTV, risk reduction, cost efficiency, and productivity, linking each initiative to quantified value.
- Provide data-driven narratives on progress, gaps, and interventions for leadership reviews and strategic forums.
- Institutionalize model governance, documentation, versioning, and monitoring in line with regulatory expectations (RBI, DPDPA etc).
- Embed responsible-AI principles — fairness, explainability, data privacy, and security — across the AI lifecycle.
- Partner with Risk, Compliance, and Information Security to ensure AI deployments meet legal, regulatory, and contractual requirements.
- On-time & in-full (OTIF) delivery of committed AI interventions.
- Measurable impact on acquisition, CLTV, risk reduction, cost efficiency, and productivity driven by AI.
- Strength of the Business–IT partnership, and maturity of data and AI infrastructure enabling scale.
- Continuity of implementation with minimal downtime and risk discovery; robustness of AI governance, model reliability, and responsible-AI compliance.
- Technical Skills / Experience / Certifications
- Strong understanding of AI/ML and GenAI concepts, use-case design, and end-to-end AI solution delivery.
- Working knowledge of data engineering, ML Ops, model deployment, and cloud/AI infrastructure fundamentals.
- Strong program delivery, dependency management, and stakeholder governance experience.
- Advanced analytics, GenAI solutioning, or digital transformation
- Program governance
- Delivery management and OTIF execution for multi-workstream initiatives
- Model governance
- Regulatory expectations (RBI, DPDPA), and Enterprise data governance
Skills Required
- Bachelor's degree in Engineering, Computer Science, Data Science, Mathematics, Statistics, or a related technical discipline
- Strong understanding of AI/ML and GenAI concepts, use-case design, and end-to-end AI solution delivery
- Working knowledge of data engineering, MLOps, model deployment, and cloud or AI infrastructure fundamentals
- Strong program delivery, dependency management, and stakeholder governance experience
- Experience in advanced analytics, GenAI solutioning, or digital transformation
- Experience with program governance, delivery management, and OTIF execution for multi-workstream initiatives
- Experience with model governance, regulatory expectations, and enterprise data governance
- Master's degree in MBA, Analytics, AI/ML, Data Science, or a related field
- Certifications in AI/ML, GenAI, data engineering, AWS, Azure, GCP, or MLOps
- Industry experience in BFSI or fintech
What We Do
SBI Card was launched in 1998 with the State Bank of India, India's largest bank, as the majority stakeholder. In March 2020, SBI Card was listed on BSE and NSE. Today, SBI Card is India’s largest pure-play credit card issuer with over 20 million cards in force, as of December 2024. Its wide array of products and services caters to a diverse range of customer segments across India, right from new-to-credit to super premium. The SBI Card brand is based on the value proposition of 'Make Life Simple'. The proposition manifests in SBI Card’s continuous efforts to simplify the lives of its customers, employees and other key stakeholders. Customer-centricity, supported by the values of trust and transparency, is core to SBI Card’s ethos.






