Position Summary
The Associate Vice President serves as the finance-side leader for artificial intelligence, reporting, and strategy within the Finance Process & Operations (FPO) team. This is the business counterpart to Carlyle’s Corporate Services AI engineering function: where engineering builds the firm’s next generation of AI-native products, this role owns the finance strategy behind them - identifying the highest-leverage opportunities, translating ambiguous business problems into scoped solutions, orchestrating delivery in partnership with engineering, and owning adoption and realized value across finance.
The AVP sets the AI vision and roadmap for FPO’s finance functions, picking the spots where AI and automation deliver the biggest payoff and focusing the firm’s investment on a few high-impact workflows. The role pairs this strategic mandate with a build-and-ship orientation, leading a small, agile team that forms quickly around high-priority finance challenges and delivers solutions at speed. Carlyle is investing aggressively in the talent, tooling, and platforms required to win here, and this role is at the center of that investment.
Success requires the judgment to prioritize ruthlessly, the credibility to partner with senior finance and technology leaders, and intellectual honesty about what AI can and cannot reliably do today. The AVP leads and develops a team, governs how finance AI is built and used, and communicates strategy and progress at the highest levels of the firm.
In-Office Requirement: 4 days a week
Responsibilities
AI Strategy & Opportunity Leadership (~25%)
- Own the AI vision, strategy, and roadmap for the FPO finance functions, picking the spots where AI and automation deliver the biggest payoff and sequencing investment accordingly
- Maintain a prioritized portfolio of AI use cases across finance, scoring opportunities on value, feasibility, and effort to focus the firm’s enterprise muscle on a few high-impact workflows
- Go narrow and deep on priority workflows, rethinking how the work is done with an AI-first lens rather than bolting AI onto existing steps
- Define a small set of outcome metrics leadership tracks - such as cycle-time, accuracy, exception volume, and throughput - and tie every AI initiative back to measurable business value
- Continuously scan the AI landscape - models, agentic frameworks, and tools - and bring promising, matured capabilities into finance practice
Finance Strategic Partner & Translation (~20%)
- Act as a trusted AI advisor to finance leadership and function heads, reimagining workflows alongside the people who run them
- Embed with finance stakeholders to identify, scope, and frame the highest-leverage AI opportunities, translating ambiguous business problems into clear, well-structured solution requirements
- Serve as the bridge between finance end-users and the Corporate Services AI engineering team, ensuring what gets built is what teams will actually use
- Maintain executive presence - ask the right questions, push back when needed, and earn trust at senior levels of the firm
- Champion intellectual honesty about AI: what current models can and cannot reliably do, and where AI is the right tool versus traditional automation or process redesign
Dynamic Tactical Problem-Solving Team (~15%)
- Lead a small, agile, cross-functional team that forms quickly around high-priority finance challenges and delivers solutions at speed - a capability the firm is actively investing in
- Foster a build-and-ship, fast-iteration culture: prototype quickly, prove value in weeks, and scale what works
- Prioritize incoming problems so the team focuses on the highest-impact, most time-sensitive opportunities first
- Bring structure, judgment, and decisiveness to ambiguous problems, operating with autonomy and a bias for action
- Pull in the right cross-functional expertise - finance SMEs, GTS/AI engineering, data, controls - for each problem rather than working in a silo
Solution Orchestration & Delivery (~20%)
- Orchestrate AI solution delivery end to end - discovery, requirements, design, build with engineering, testing, deployment, adoption, and iteration
- Establish reusable building blocks for finance AI - use-case assessment frameworks, prompt and agent patterns, evaluation criteria, and adoption playbooks - so each new solution starts further ahead than the last
- Run agile delivery for the team’s portfolio, balancing rapid tactical wins against the strategic roadmap
- Partner with GTS/AI engineering, data, and security teams so solutions deploy cleanly and meet enterprise standards for controls, observability, and audit
- Drive adoption and change management - training, communication, and workflow redesign - to convert delivered solutions into realized value, closing the gap between pilot and production
AI Governance & Risk Mitigation (~10%)
- Establish and enforce the standards, patterns, and guardrails for how finance AI solutions are built and used, balancing speed with maintainability, security, and responsible-AI practices
- Set governance for AI data usage, access, model validation, and human oversight, partnering with risk, compliance, and GTS
- Identify and mitigate risks specific to AI in finance - model drift, output reliability, data quality, and control gaps - and design around model limitations
- Govern citizen-built and shadow AI within finance, bringing it into a safe, supported framework
- Ensure AI solutions preserve auditability and compliance across the finance processes they touch
People Leadership & Communication (~10%)
- Lead, develop, and grow the team, setting direction, allocating work, coaching, and owning team members’ growth and performance
- Build a high-performing team and raise the bar through mentorship, clear standards, and constructive feedback
- Attract, hire, and retain strong talent, and build a team culture where people do their best work
- Foster a culture grounded in customer focus, fast iteration, collaboration, and intellectual honesty
- Communicate strategy, progress, wins, and lessons learned to finance and firm leadership, tailoring to executive audiences
- Represent FPO in the firm’s broader AI and engineering community of practice, sharing patterns and lessons, learning from counterparts, and avoiding duplicate work
Qualifications
Education & Certificates
- Bachelor’s degree required
- Concentration a technical or quantitative field, preferred
Professional Experience
- 7+ years overall relevant experience, required
- Experience across finance/accounting operations, financial systems, and process improvement, with a strong track record of applying AI and automation to real business problems, preferred
- Demonstrated experience setting AI strategy and prioritizing use cases - not just using the tools - and translating ambiguous business problems into shipped solutions
- Experience leading delivery in partnership with engineering or technical teams; able to scope, orchestrate, and drive AI and automation builds through to adoption
- Hands-on fluency with generative AI tools and concepts - prompt engineering, agentic workflows, RAG, and document intelligence - at an applied level; developer-grade coding not required
- Experience leading and developing a team, including direct people management or team-lead responsibility
- Strong understanding of finance and accounting processes, reporting, data integrity, and controls
- Experience with AI governance, responsible-AI practices, data quality, and change management
- Familiarity with agile delivery and tools such as Jira; SQL and data fluency a plus
- Background in private equity, financial services, or alternative investment operations preferred
Competencies & Attributes
- Builder’s instinct under ambiguity - measures progress in working solutions, and can turn a vague business problem into a working prototype quickly
- Customer obsession - sits with users, reimagines workflows alongside them, and ships solutions that work in the real world
- Leverage mindset - sees every use case as an opportunity to make the next one faster, investing in reusable building blocks that compound across the team’s work
- •Executive presence - can sit across from a senior leader, ask the right questions, push back when needed, and earn trust
- Intellectual honesty about AI - knows what current models can and cannot do, designs around their limits, and does not confuse demo magic with production reliability
- Proficiency with generative AI tools such as ChatGPT, Claude, or Microsoft Copilot, and a conceptual understanding of how large language models work
- Strong stakeholder management and influencing skills across functions, seniority levels, and the broader firm
- Excellent written and verbal communication, with the ability to tailor messaging across finance, technology, and senior leadership audiences
- Advanced Excel and PowerPoint; strong presentation skills
- Demonstrated ability to lead and develop both direct reports and indirect or consultant team members
- Highly organized with strong attention to detail, able to manage multiple priorities in a fast-paced environment
Benefits/Compensation
The compensation range for this role is specific to Washington, DC and takes into account a wide range of factors including but not limited to the skill sets required/preferred; prior experience and training; licenses and/or certifications.
The anticipated base salary range for this role is $150,000 to $170,000.
In addition to the base salary, the hired professional will enjoy a comprehensive benefits package spanning retirement benefits, health insurance, life insurance and disability, paid time off, paid holidays, family planning benefits and various wellness programs. Additionally, the hired professional may also be eligible to participate in an annual discretionary incentive program, the award of which will be dependent on various factors, including, without limitation, individual and organizational performance.
Due to the high volume of candidates, please be advised that only candidates selected to interview will be contacted by Carlyle.
