SVP – Digital Platforms — Machine-Learning Infrastructure Stack

The mandate

The chair and executive committee are aligned that the immediate priority is fragmented digital platforms constraining customer and employee journeys within a listed enterprise artificial-intelligence products company. The immediate arena is the machine-learning infrastructure stack during model-cost escalation. For mandate 159, the successful executive inherits decisions that have been deferred, competing stakeholder expectations and a need to establish facts before committing further capital.

The SVP – Digital Platforms operating perimeter covers approximately ₹900 crore in AI product and services revenue, with activity spanning several machine-learning infrastructure stack customer, product and delivery clusters rather than a single asset. The SVP – Digital Platforms Artificial Intelligence remit carries direct influence over roughly 350 colleagues and third-party capacity.

The group board and the relevant risk and people committees want a SVP – Digital Platforms who can convert ambiguity into a short list of explicit choices for the machine-learning infrastructure stack. The SVP – Digital Platforms Artificial Intelligence seat must resolve model-cost escalation, while preserving the underlying strengths of the machine-learning infrastructure stack. For mandate 159, value will come through sharper allocation, stronger leaders and an operating cadence that exposes variance early.

The SVP – Digital Platforms’s first year on the machine-learning infrastructure stack is expected to end with platform adoption, reliability and measurable process simplification. In mandate 159, authority covers resources and leadership appointments; material trade-offs go directly to the board sponsor.

Why this seat is open

This is a newly created SVP – Digital Platforms — Machine-Learning Infrastructure Stack seat approved as part of the next operating model; it is not an incumbent replacement. The board is running a planned 4–6 month search so the appointee can join ahead of the next capital and talent cycle. Current leaders retain their existing accountabilities until the machine-learning infrastructure stack remit is formally activated. Confidentiality protects organisation design choices while the board compares external and adjacent-sector talent.

What you will own

• Set the SVP – Digital Platforms value-creation thesis for the machine-learning infrastructure stack, translate it into no more than five enterprise priorities and stop work that does not support them.
• Carry stewardship of approximately ₹900 crore in AI product and services revenue, including allocation, risk acceptance and board forecasts.
• Lead the SVP – Digital Platforms Artificial Intelligence organisation of about 350 employees and partners, appointing a team with clear decision rights and credible succession for every critical seat.
• Resolve the machine-learning infrastructure stack economics and execution constraints created by model-cost escalation, with SVP – Digital Platforms-approved owners, dated milestones and transparent escalation thresholds.
• Establish one SVP – Digital Platforms operating review across commercial, customer, financial, people, technology and risk outcomes for the machine-learning infrastructure stack; remove reconciliations that obscure accountability.
• Show end-to-end ownership of a material platform or value stream, including budget, talent and measurable operating outcomes in mandate 159.
• Build the SVP – Digital Platforms’s three-year succession and capability plan for the machine-learning infrastructure stack, reducing dependence on individual executives and improving mobility across the wider Artificial Intelligence organisation.

The first 12 months

• Days 1–90: Validate the machine-learning infrastructure stack baseline, meet the 30 stakeholders most consequential to fragmented digital platforms constraining customer and employee journeys, assess the leadership team, stabilise immediate delivery risks and agree a board-owned scorecard with explicit decision gates.
• Months 4–9: Make the principal SVP – Digital Platforms portfolio and organisation choices for the machine-learning infrastructure stack, install the new operating cadence, fill critical leadership gaps and deliver the first measurable release of cash, capacity or customer value.
• Months 10–12: Demonstrate a repeatable machine-learning infrastructure stack trend against platform adoption, reliability and measurable process simplification, lock the following year’s capital and talent plan, evidence control sustainability and present a credible three-year value case with downside actions.

What the board will measure

• Delivery of the SVP – Digital Platforms’s agreed first-year machine-learning infrastructure stack value case within a 10% tolerance, with variance explained before rather than after the relevant quarter closes.
• A SVP – Digital Platforms forecast that remains decision-useful across three consecutive quarters and reconciles the machine-learning infrastructure stack’s operating, cash, customer and people assumptions.
• Closure of the SVP – Digital Platforms mandate’s highest-priority machine-learning infrastructure stack risk and execution issues by their board-approved dates, with independent evidence that fixes are sustained.
• Retention of at least 90% of critical machine-learning infrastructure stack talent and ready-now successors for at least 70% of the SVP – Digital Platforms’s direct reports.
• A quantified SVP – Digital Platforms-owned improvement in the machine-learning infrastructure stack operating constraint behind model-cost escalation, supported by a clean baseline and named data owner.
• Clear stakeholder confidence in mandate 159: no unresolved high-severity escalation older than 30 days and no material surprise withheld from its agreed governance forum.

The person

You are currently a SVP Digital, Platform Head or Technology Transformation Leader in a listed Artificial Intelligence or adjacent enterprise. In relation to the machine-learning infrastructure stack, your SVP – Digital Platforms track record includes a transition where the original plan was no longer sufficient; you can explain your choices, evidence and numerical impact. Candidates from AI, enterprise software, data infrastructure, cloud, analytics or applied research will be considered where the operating model, customer stakes and governance intensity match this SVP – Digital Platforms brief.

As a SVP – Digital Platforms candidate, you bring 18–22 years of progressive Artificial Intelligence or adjacent-sector experience, consistent with the 18-22 experience band. At minimum, you have carried a P&L, book, budget or accountable portfolio of ₹750 crore and led an organisation of at least 350 people.

For mandate 159, the board wants two transitions: a difficult machine-learning infrastructure stack portfolio choice and a leadership-system change during model-cost escalation. As the prospective SVP – Digital Platforms for this machine-learning infrastructure stack, you must challenge optimistic cases and still create followership. References for mandate 159 must distinguish your contribution from the institution around you.

The SVP – Digital Platforms role in Artificial Intelligence is based in Pune; relocation is expected, although a structured weekly commute may be considered during the first quarter.

Non-negotiables

• Current or recent accountability at the level of SVP Digital, Platform Head or Technology Transformation Leader, with direct exposure to a board, investment committee or equivalent Artificial Intelligence governance forum.
• Proven SVP – Digital Platforms ownership of at least ₹750 crore and leadership of no fewer than 350 employees in a comparable machine-learning infrastructure stack context.
• One completed Artificial Intelligence or adjacent-sector example of fragmented digital platforms constraining customer and employee journeys with outcomes sustained for at least two reporting periods after the initial intervention.
• Sector credibility from AI, enterprise software, data infrastructure, cloud, analytics or applied research; experience that is purely functional and lacks SVP – Digital Platforms-level machine-learning infrastructure stack consequences will not meet the bar.
• Willingness to meet the Pune location expectation, complete conflicts and background diligence, and protect the confidentiality of mandate 159.

Compensation and terms

The anticipated SVP – Digital Platforms package is ₹2.2–3.0 crore fixed + performance variable, calibrated to the final machine-learning infrastructure stack scope and the candidate’s current mix. Any long-term participation for mandate 159 follows standard vesting and performance conditions. The SVP – Digital Platforms appointment in Pune, centred on the machine-learning infrastructure stack, offers regular exposure to the group board and the relevant risk and people committees. A notice period of up to 6 months can be accommodated for the selected executive in mandate 159.

Confidentiality

The client name, precise footprint and transaction history are outside this brief for mandate 159. They will be shared with qualified candidates under a mutual undertaking, and the composite facts here must not be reverse-engineered or circulated for mandate 159.