New opportunity

AI Firm Deployed Engineer, Private Capital Markets

Intapp · New York, United States of America

About this role

Intapp seeks an AI Firm Deployed Engineer to help private equity, venture capital, and private credit firms put AI to work across the investment lifecycle. The role combines private capital experience with hands-on deployment of Intapp Celeste’s AI agents, coworkers, playbooks, and workflows. It is a full-time, hybrid (partially remote) position listed in US NY New York.

Responsibilities

Advise firm leaders, investment committees, operations teams, and portfolio managers on where AI can add value in deal sourcing, execution, monitoring, and LP relations.
Configure Celeste agents and playbooks around each firm’s investment methodology, data standards, and risk framework. Map transactions from sourcing through close into AI-ready processes, identifying opportunities for automation and decisions that require human judgment.
Build adoption through practitioner-focused walkthroughs and hands-on training for investment and operational teams.
Share insights from deal work with Intapp’s Industry Architects and Product leaders to inform Celeste’s product strategy.

Ideal background:

Substantive experience in private equity, venture capital, private credit, real assets, or investment banking, including hands-on exposure to deal execution, underwriting, portfolio management, or fund operations. No specific number of years or education requirement is stated.
Understanding of sourcing, structuring, diligence, monitoring, LP reporting, fund performance, and business development.
Experience applying AI and LLMs to investment or commercial workflows, such as market analysis, deal screening, memo drafting, or process design, with sound judgment about accuracy and risk. Practical fluency in prompt engineering, AI workflow design, and agentic tools to configure and evaluate deployments without writing code.
Communication and judgment to advise senior decision-makers, earn the trust of investment professionals, and work in open-ended client-facing situations.

Intapp offers training and continuing-education reimbursement, complimentary office lunches, wellness and flexible time-off programs, family-formation support, paid volunteer time, and donation matching. Base salary is not specified; variable compensation and equity may also be included.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI agentsIntapp CelestePrompt engineeringAI workflow designAgentic toolingLarge language modelsWorkflow automationDeal sourcingDeal executionUnderwritingPortfolio managementFund operationsLP reportingDeal screeningMarket analysisStakeholder advisoryClient trainingRisk assessment

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Find valuable AI use cases across the investment lifecycle
Advise firm leaders, investment committees, operations teams, and portfolio managers on opportunities in sourcing, deal execution, monitoring, and LP relations.
Deploy Celeste around each firm's practices
Lead configuration of Celeste agents, coworkers, and playbooks to reflect a firm's investment methodology, data standards, and risk framework.
Turn deal work into AI-ready workflows
Map transactions from sourcing through close, identify where automation can accelerate execution, and distinguish steps that require human judgment.
Build practitioner adoption
Earn investment and operational teams' trust through walkthroughs and hands-on training tailored to how each group works.
Feed field experience into product strategy
Work with Intapp Industry Architects and Product leaders so Celeste reflects the realities of private capital workflows.

Likely challenges to think through

Capturing unwritten investment judgment
Likely challenge, not a confirmed employer problem: a firm's sourcing and diligence methods may resist standardization, while agents need explicit instructions. Prepare to describe how you would map a deal process with practitioners, document exceptions, and keep consequential decisions with humans.
Balancing speed with dependable outputs
Likely challenge, not a confirmed employer problem: automating screening or memo drafting could save time, but errors or mishandled deal information carry consequences. Prepare an approach to testing outputs against source material and aligning a deployment with the firm's data standards and risk framework.
Earning trust across different teams
Likely challenge, not a confirmed employer problem: senior decision-makers may want strategic value while deal and operations teams need workflows they can use. Prepare to explain how you would tailor a walkthrough to each audience, gather objections, and revise the deployment without asking practitioners to change their methods solely for the technology.

Practice your answers

Technical practice

How would you prioritize AI opportunities across deal sourcing, execution, monitoring, and LP relations?

What to coverPreparation suggestion: Compare potential value, workflow readiness, data quality, and the consequences of an incorrect output. Explain how you would seek input from investment, operations, and firm leaders before choosing where to start.

How would you map a transaction from sourcing through close into an AI-ready workflow?

What to coverPreparation suggestion: Identify the information, handoffs, and decisions at each stage, then separate repeatable tasks from judgments that belong with deal professionals. Explain where you would check that the mapped process reflects the firm's actual practices rather than an assumed standard.

How would you configure Celeste agents and playbooks around a firm's investment methodology?

What to coverPreparation suggestion: Describe how you would elicit the firm's criteria, terminology, data standards, and risk framework before configuring a workflow. Discuss how you would test whether its outputs reflect those choices and revise the configuration when they do not.

How would you design an AI workflow for market analysis or deal screening?

What to coverPreparation suggestion: Walk through the inputs, prompts, agent steps, and expected output for one of these use cases. Explain how you would make assumptions visible and keep the result useful for a professional making the decision.

Where can LLMs help with underwriting, diligence, or investment memo drafting, and where should they not decide?

What to coverPreparation suggestion: Distinguish organizing and summarizing evidence from validating assumptions or reaching an investment conclusion. Discuss the accuracy and transaction risks that would warrant source checks and human review.

How would you assess an AI-assisted portfolio-monitoring workflow?

What to coverPreparation suggestion: Describe what information a portfolio team would need, what changes an agent might surface, and how a professional would verify them. Consider the trade-off between faster review and the risk of missed or misleading signals.

How would you approach AI support for LP reporting and fund-performance work?

What to coverPreparation suggestion: Explain which drafting or information-gathering tasks might be repeatable and which figures or statements need careful validation. Address consistency with firm data standards and review before information reaches LPs.

An agent produces a plausible but inaccurate deal summary; how would you diagnose and address it?

What to coverPreparation suggestion: Trace the source material, firm context, prompt or workflow instructions, and output to locate the error. Explain how you would test a revision against deal-professional expectations rather than relying on a more fluent answer.

How would you handle sensitive deal information when deploying an agentic workflow?

What to coverPreparation suggestion: Discuss proprietary knowledge, access controls, the firm's governance framework, and applicable regulatory requirements as deployment considerations named on the page. Explain how you would check that an agent uses appropriate context without exposing information to the wrong users.

Behavioural practice

Tell me about a time you earned the trust of a senior stakeholder who was skeptical of a proposed approach.

What to coverPreparation suggestion: Use a real situation, your actions to understand the stakeholder's concerns and explain the trade-offs, and the result. Focus on how you communicated with investment professionals or other decision-makers rather than claiming that skepticism disappeared.

Tell me about a time you helped different groups adopt a new workflow.

What to coverPreparation suggestion: Describe the situation, how you tailored walkthroughs or hands-on support to each group's work, and the result. Distinguish what you did for practitioners from what you did for operational stakeholders.

Tell me about a time you had to exercise judgment in an open-ended, client-facing situation.

What to coverPreparation suggestion: Give the situation, the uncertainty or risk you faced, the action you took, and the result. Explain what you verified before offering advice and where you sought input instead of making an unsupported assumption.

Tell me about a time you translated frontline workflow insights into a product or process improvement.

What to coverPreparation suggestion: State the situation, how you gathered and communicated the insight to collaborators, what action followed, and the result. Emphasize the practical workflow problem rather than assuming every suggestion became a product feature.

Tell me about a time stakeholders disagreed about what to automate or how much review to retain.

What to coverPreparation suggestion: Use a real situation, your actions to surface each group's priorities and the risks, and the result. Explain how you balanced efficiency with the need for professional judgment.

Sharpen your knowledge

Topics to prepare

Private capital deal lifecycle
The posting calls for understanding how deals are sourced, structured, diligenced, executed, and monitored. Revision exercise: trace a hypothetical transaction from sourcing through close and portfolio oversight; identify the decisions and information needed at each stage.
Fund operations and LP relationships
The posting links investment work to LP reporting, fund performance, and business development. Revision exercise: explain how a change in portfolio performance would affect internal monitoring and an LP update, distinguishing the audiences and information needs.
Celeste’s role in firm-specific workflows
The role deploys Celeste agents, coworkers, and playbooks around each firm’s investment methodology, data standards, and risk framework. Revision exercise: outline what firm-specific context and rules an agent would need to support deal screening; do not assume access to Celeste.
AI accuracy, governance, and human judgment
The posting emphasizes evaluating LLM outputs for accuracy and consequence, respecting firm data and governance, and deciding where human judgment remains essential. Revision exercise: review a hypothetical AI-generated investment memo and mark unsupported claims, sensitive information, and conclusions requiring deal-team approval.

Skills to revise

Map a deal workflow for automation
The role requires translating transactions from sourcing through close into AI-ready processes. Practice suggestion: draw a hypothetical diligence workflow with inputs, handoffs, potential automation points, and explicit human decision gates.
Iterate prompts and evaluate outputs
The posting requests practical prompt-engineering and agentic-tooling fluency without writing code. Practice suggestion: draft and revise a deal-screening prompt using hypothetical information, then score its outputs for factual support, omissions, and decision risk.
Advise senior investment stakeholders
The role advises managing directors, investment committees, operations leaders, and portfolio managers on AI value. Practice suggestion: deliver a two-minute recommendation for one monitoring use case, explaining its value, limitations, and where a professional must retain judgment.
Run practitioner-focused training
The posting calls for walkthroughs and hands-on training tailored to investment and operational teams. Practice suggestion: plan a short walkthrough of an AI-assisted memo-drafting process, with separate examples and review responsibilities for each audience.
Turn field observations into product feedback
The role shares deal-work insights with Intapp Industry Architects and Product leaders. Practice suggestion: write a concise feedback note describing a hypothetical workflow friction point, its effect on deal teams, and a proposed improvement; treat the scenario as interview preparation, not a claim about Celeste.

Your preparation checklist

Prepare a private-capital deal walkthrough
The role calls for hands-on investment experience. Prepare an anonymized example, if available, showing your contribution to sourcing, underwriting, execution, monitoring, or fund operations and how the work fed into investment decisions or LP reporting.
Map a transaction from sourcing to close
Draft a one-page workflow showing inputs, owners, decisions, and handoffs. Mark where AI might accelerate work and where a deal professional must retain judgment; this is a preparation exercise based on the role’s workflow-mapping responsibility.
Sketch a firm-specific Celeste deployment
Prepare a hypothetical agent or playbook configuration tied to an investment methodology, data standards, and risk framework. Identify what firm context you would need to learn first; do not assume access to Celeste or a client’s data.
Document an AI workflow and its checks
If you have a relevant example, show the prompt or workflow iterations for market analysis, deal screening, memo drafting, or process design. Include how you checked accuracy, handled sensitive information, and recognized consequential errors.
Rehearse a stakeholder walkthrough
Outline a short, practitioner-focused demonstration for investment and operations teams, then practice explaining its value and limits to a skeptical senior decision-maker. The role explicitly involves advisory work and hands-on training.
Prepare product-feedback examples
Draft two or three observations from deal work that could inform a Celeste agent or playbook. State the practitioner problem, proposed change, and how you would convey it to Industry Architects and Product leaders.
Write targeted questions for Intapp
Ask how the team learns each firm’s unwritten methods, evaluates agent outputs against its risk framework, divides decisions between agents and people, and passes deployment insights to Product. These questions follow from the advertised responsibilities.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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