EVP – Sustainability and Transition — Machine-Learning Infrastructure Stack

The mandate

The next planning cycle has brought into focus transition commitments that are not yet embedded in capital decisions within a listed enterprise artificial-intelligence products company. The immediate arena is the machine-learning infrastructure stack during a post-funding scale-up. For mandate 183, the successful executive inherits decisions that have been deferred, competing stakeholder expectations and a need to establish facts before committing further capital.

The EVP – Sustainability and Transition operating perimeter covers approximately CHF 1,000 million 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 EVP – Sustainability and Transition Artificial Intelligence remit carries direct influence over roughly 175 colleagues and third-party capacity.

The group board and the relevant risk and people committees want a EVP – Sustainability and Transition who can convert ambiguity into a short list of explicit choices for the machine-learning infrastructure stack. The EVP – Sustainability and Transition Artificial Intelligence seat must resolve a post-funding scale-up, while preserving the underlying strengths of the machine-learning infrastructure stack. For mandate 183, value will come through sharper allocation, stronger leaders and an operating cadence that exposes variance early.

The EVP – Sustainability and Transition’s first year on the machine-learning infrastructure stack is expected to end with credible transition economics, delivery governance and auditable progress. In mandate 183, 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 EVP – Sustainability and Transition — Machine-Learning Infrastructure Stack seat, established because a post-funding scale-up 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 EVP – Sustainability and Transition 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 CHF 1,000 million in AI product and services revenue, including allocation, risk acceptance and board forecasts.
• Lead the EVP – Sustainability and Transition Artificial Intelligence organisation of about 175 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 post-funding scale-up, with EVP – Sustainability and Transition-approved owners, dated milestones and transparent escalation thresholds.
• Establish one EVP – Sustainability and Transition operating review across commercial, customer, financial, people, technology and risk outcomes for the machine-learning infrastructure stack; remove reconciliations that obscure accountability.
• Demonstrate enterprise authority across functions and markets, with outcomes visible in cash, customers or controlled risk in mandate 183.
• Build the EVP – Sustainability and Transition’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 transition commitments that are not yet embedded in capital decisions, assess the leadership team, stabilise immediate delivery risks and agree a board-owned scorecard with explicit decision gates.
• Months 4–9: Make the principal EVP – Sustainability and Transition 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 credible transition economics, delivery governance and auditable progress, 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 EVP – Sustainability and Transition’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 EVP – Sustainability and Transition 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 EVP – Sustainability and Transition 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 EVP – Sustainability and Transition’s direct reports.
• A quantified EVP – Sustainability and Transition-owned improvement in the machine-learning infrastructure stack operating constraint behind a post-funding scale-up, supported by a clean baseline and named data owner.
• Clear stakeholder confidence in mandate 183: 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 EVP Sustainability, Transition Director or Strategy Leader in a listed Artificial Intelligence or adjacent enterprise. In relation to the machine-learning infrastructure stack, your EVP – Sustainability and Transition 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 EVP – Sustainability and Transition brief.

As a EVP – Sustainability and Transition 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 CHF 700 million and led an organisation of at least 125 people.

For mandate 183, the board wants two transitions: a difficult machine-learning infrastructure stack portfolio choice and a leadership-system change during a post-funding scale-up. As the prospective EVP – Sustainability and Transition for this machine-learning infrastructure stack, you must challenge optimistic cases and still create followership. References for mandate 183 must distinguish your contribution from the institution around you.

The EVP – Sustainability and Transition must be based in Zurich; international relocation is supported, but this Artificial Intelligence role is not designed as a remote appointment.

Non-negotiables

• Current or recent accountability at the level of EVP Sustainability, Transition Director or Strategy Leader, with direct exposure to a board, investment committee or equivalent Artificial Intelligence governance forum.
• Proven EVP – Sustainability and Transition ownership of at least CHF 700 million and leadership of no fewer than 125 employees in a comparable machine-learning infrastructure stack context.
• One completed Artificial Intelligence or adjacent-sector example of transition commitments that are not yet embedded in capital decisions 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 EVP – Sustainability and Transition-level machine-learning infrastructure stack consequences will not meet the bar.
• Willingness to meet the Zurich location expectation, complete conflicts and background diligence, and protect the confidentiality of mandate 183.

Compensation and terms

The anticipated EVP – Sustainability and Transition package is CHF 320,000–430,000 base + annual incentive, calibrated to the final machine-learning infrastructure stack scope and the candidate’s current mix. Any long-term participation for mandate 183 follows standard vesting and performance conditions. The EVP – Sustainability and Transition appointment in Zurich, 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 183.

Confidentiality

This search is being conducted without naming the client for mandate 183. Identifying information will follow only when both sides elect to proceed under confidentiality; nothing in the published mandate should be treated as a clue to ownership or brand for mandate 183.