Chief Operating Officer — Machine-Learning Infrastructure Stack

The mandate

The board has concluded that incremental adjustment will not resolve operating complexity that has outgrown the current governance model within a listed enterprise artificial-intelligence products company. The immediate arena is the machine-learning infrastructure stack during a responsible-AI control build. For mandate 171, the successful executive inherits decisions that have been deferred, competing stakeholder expectations and a need to establish facts before committing further capital.

The Chief Operating Officer operating perimeter covers approximately ₹1,200 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 Chief Operating Officer Artificial Intelligence remit carries direct influence over roughly 1,000 colleagues and third-party capacity.

The group board and the relevant risk and people committees want a Chief Operating Officer who can convert ambiguity into a short list of explicit choices for the machine-learning infrastructure stack. The Chief Operating Officer Artificial Intelligence seat must resolve a responsible-AI control build, while preserving the underlying strengths of the machine-learning infrastructure stack. For mandate 171, value will come through sharper allocation, stronger leaders and an operating cadence that exposes variance early.

The Chief Operating Officer’s first year on the machine-learning infrastructure stack is expected to end with delivery reliability, productivity and end-to-end accountability. In mandate 171, 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 Chief Operating Officer — Machine-Learning Infrastructure Stack seat, established because a responsible-AI control build now requires one accountable executive rather than distributed ownership. The board has classified the appointment as urgent and intends to move from qualified shortlist to offer within 6–8 weeks. Interim governance protects the machine-learning infrastructure stack, but it is not a substitute for a permanent appointee. The external search remains confidential to avoid unnecessary disruption before the appointment is agreed.

What you will own

• Set the Chief Operating Officer 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 ₹1,200 crore in AI product and services revenue, including allocation, risk acceptance and board forecasts.
• Lead the Chief Operating Officer Artificial Intelligence organisation of about 1,000 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 a responsible-AI control build, with Chief Operating Officer-approved owners, dated milestones and transparent escalation thresholds.
• Establish one Chief Operating Officer operating review across commercial, customer, financial, people, technology and risk outcomes for the machine-learning infrastructure stack; remove reconciliations that obscure accountability.
• Have carried end-to-end delivery accountability across multiple sites, channels or markets with quantified service and cost outcomes in mandate 171.
• Build the Chief Operating Officer’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 operating complexity that has outgrown the current governance model, assess the leadership team, stabilise immediate delivery risks and agree a board-owned scorecard with explicit decision gates.
• Months 4–9: Make the principal Chief Operating Officer 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 delivery reliability, productivity and end-to-end accountability, 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 Chief Operating Officer’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 Chief Operating Officer 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 Chief Operating Officer 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 Chief Operating Officer’s direct reports.
• A quantified Chief Operating Officer-owned improvement in the machine-learning infrastructure stack operating constraint behind a responsible-AI control build, supported by a clean baseline and named data owner.
• Clear stakeholder confidence in mandate 171: 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 COO, EVP Operations or Business Operations President in a listed Artificial Intelligence or adjacent enterprise. In relation to the machine-learning infrastructure stack, your Chief Operating Officer 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 Chief Operating Officer brief.

As a Chief Operating Officer 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 ₹1,250 crore and led an organisation of at least 1,000 people.

For mandate 171, the board wants two transitions: a difficult machine-learning infrastructure stack portfolio choice and a leadership-system change during a responsible-AI control build. As the prospective Chief Operating Officer for this machine-learning infrastructure stack, you must challenge optimistic cases and still create followership. References for mandate 171 must distinguish your contribution from the institution around you.

The Chief Operating Officer role in Artificial Intelligence is based in Bengaluru; 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 COO, EVP Operations or Business Operations President, with direct exposure to a board, investment committee or equivalent Artificial Intelligence governance forum.
• Proven Chief Operating Officer ownership of at least ₹1,250 crore and leadership of no fewer than 1,000 employees in a comparable machine-learning infrastructure stack context.
• One completed Artificial Intelligence or adjacent-sector example of operating complexity that has outgrown the current governance model 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 Chief Operating Officer-level machine-learning infrastructure stack consequences will not meet the bar.
• Willingness to meet the Bengaluru location expectation, complete conflicts and background diligence, and protect the confidentiality of mandate 171.

Compensation and terms

The anticipated Chief Operating Officer package is ₹3.2–4.6 crore fixed + performance variable and LTI, calibrated to the final machine-learning infrastructure stack scope and the candidate’s current mix. Any long-term participation for mandate 171 follows standard vesting and performance conditions. The Chief Operating Officer appointment in Bengaluru, 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 171.

Confidentiality

The organisation will be identified only after reciprocal interest and a confidentiality undertaking for mandate 171. The market, scale and situation in this brief are intentionally composite and are not a coded description of a named enterprise for mandate 171.