Vice President, AI Solutions

What matters most

• Turn advisory methods into AI products

  The role leads agentic AI solutions and automation workflows that encode the firm’s financial and operational advisory methodologies into scalable, reliable platforms.

• Own delivery through measurable impact

  The VP owns workstreams from requirements and prototypes through deployment, monitoring and iteration, tracking performance, adoption and ROI.

• Set the technical bar and develop the team

  Responsibilities include using AI coding tools, establishing engineering practices, evaluating platforms and orchestration tools, reviewing code and mentoring junior team members.

• Build responsible AI into production

  The role defines and enforces standards for prompt safety, data privacy, bias mitigation and responsible AI across workstreams.

• Connect technical work to firm needs

  The VP works with practice leaders, Delivery Enablement and domain experts, communicates with senior stakeholders, and supports training, documentation, proposals and demonstrations.

Likely challenges to think through

• Scaling methods without losing nuance

  A likely challenge is turning expert-led advisory methods into repeatable workflows while preserving judgment for cases that do not fit a standard pattern. Prepare to explain how you would define workflow boundaries, involve domain experts and evaluate output quality.

• Balancing rapid development with controls

  AI coding tools and emerging platforms can speed delivery, while production reliability, privacy and prompt safety require scrutiny. Prepare an example of how you would compare tools, review generated code and apply evaluation and governance before deployment.

• Proving value across distributed stakeholders

  Coordinating Gurgaon-based work with US teams and senior practice stakeholders may make feedback and adoption harder to align. Prepare to describe how you would agree on success measures, communicate trade-offs across time zones and use adoption and ROI evidence to guide iteration.

Topics to prepare

• Agentic AI architecture

  Sketch an LLM-powered workflow that turns a financial or operational advisory methodology into an agentic platform; identify its steps, dependencies and failure points. The posting calls for scalable, reliable agentic solutions that encode the firm’s methodologies.

• AI product lifecycle

  Map a hypothetical workstream from requirements and prototype to deployment, monitoring and iteration, noting the decision at each stage. The role owns this full lifecycle rather than only model development.

• Model evaluation and MLOps

  Draft an evaluation plan covering output quality, operational performance and monitoring after release. The qualifications explicitly call for model evaluation, MLOps and data-engineering understanding.

• Responsible AI governance

  Work through a hypothetical sensitive-data workflow and specify how you would address prompt safety, privacy, bias detection and explainability. The role defines governance standards and embeds these principles in production systems.

• Impact measurement

  Design a scorecard for a hypothetical advisory automation, distinguishing solution performance, adoption and ROI; state what feedback would trigger an iteration. The posting explicitly requires tracking these measures and improving products with stakeholder feedback.

Skills to revise

• Build and inspect an LLM workflow

  Practice implementing a small automation in Python or JavaScript using a posting-listed API or orchestration option, then explain integration and scaling trade-offs. The role requires strong development skills and experience delivering LLM and agentic workflows at scale.

• Use AI coding tools with review discipline

  Use Claude Code or a comparable AI coding tool to draft a change, then review the resulting code for correctness, maintainability and safety. Expert use of AI coding tools, engineering best practices and code review are explicit expectations.

• Translate stakeholder requirements

  Practice turning a hypothetical request from an advisory practice leader into a short feature specification with assumptions, acceptance criteria and questions for domain experts. The role works across practice leaders, Delivery Enablement and domain experts.

• Lead technical delivery

  Rehearse a code-review discussion that gives a junior developer actionable feedback while resolving a workstream dependency. The posting calls for mentoring, technical guidance and cross-functional delivery.

• Present a solution to nontechnical stakeholders

  Prepare a brief demonstration and executive-ready summary explaining a proposed workflow, its limitations and its impact measures without relying on technical jargon. The role includes client presentations, demonstrations and communication with senior stakeholders.

• Map the role to your evidence

  Create a one-page inventory of examples relevant to the posting’s LLM applications, agentic workflows, engineering leadership and cross-functional delivery. Mark any area without a direct example rather than implying experience.

• Prepare an end-to-end project walkthrough

  If you have a relevant delivered project, outline requirements, prototype, architecture, deployment, monitoring and iteration. Identify your decisions, team contributions and measurable results without sharing confidential material.

• Practice a financial or operational advisory design exercise

  Sketch a hypothetical agentic workflow that translates an advisory methodology into product features. Show how you would gather requirements from practice leaders and domain experts, set boundaries for the agent, and move from prototype to a reliable production system.

• Demonstrate engineering and tool judgment

  Prepare a concise technical explanation of your Python or JavaScript work, use of AI coding tools such as Claude Code, code-review approach, and criteria for selecting an LLM platform or orchestration tool. Distinguish tools you have used from those you would evaluate.

• Build a governance and measurement plan

  Draft a sample plan covering prompt safety, privacy, bias detection, explainability, evaluation and monitoring. Include proposed performance, adoption and ROI measures; these are preparation suggestions, not specified company targets.

• Rehearse a leadership and executive briefing

  Prepare an example of mentoring or coordinating a cross-functional workstream, plus a two-minute, nontechnical explanation of an AI solution’s value and risks. The posting also calls for training, documentation, proposals and client demonstrations.

• Bring focused questions for the employer

  Ask which advisory workflows are priorities, how the Director of AI Solutions and practice leaders divide decisions, what production governance and success measures are in place, and how the hybrid Gurgaon team collaborates with US-based colleagues.