Manager, Global Private Equity Data and AI Engineer

Company Profile

The Carlyle Group (NASDAQ: CG) is a global investment firm with $485 billion of assets under management, across 678 investment vehicles as of June 30, 2026. Founded in 1987 in Washington, DC, Carlyle has grown into one of the world's largest and most successful investment firms, with more than 2,500 professionals operating in 28 offices in North America, Europe, the Middle East, Asia and Australia.

Carlyle’s purpose is to connect people, ideas, and capital to fuel growth for companies and performance for investors, which range from public and private pension funds to wealthy individuals and families to sovereign wealth funds, unions and corporations. Carlyle invests across three segments – Global Private Equity, Global Credit and Carlyle AlpInvest – and has deep expertise across industries, markets, and geographies.

At Carlyle, we believe that a wide spectrum of experiences and viewpoints drives performance and success. Our CEO, Harvey Schwartz, has stated that, "To build better businesses and create value for all of our stakeholders, we are focused on assembling leadership teams with the strongest insights from a range of perspectives." Reflecting this view, emphasis is placed on development, retention and inclusion through our internal processes and seven Employee Resource Groups (ERGs). We cultivate a culture where ideas are openly shared and challenged, connecting diverse expertise and perspectives to drive enduring value.

Position Summary:

The Carlyle Group seeks a hands-on engineering manager to lead AI and data engineering for Global Private Equity (GPE). The team builds and runs the platform that brings together portfolio company, deal, fund, valuation and performance data from Carlyle’s investment, portfolio monitoring and enterprise systems, and delivers it to investment teams, valuations, fund finance, investor relations and leadership through reporting, applications and AI-enabled tools. The role designs and ships production AI capabilities, including Model Context Protocol (MCP) services, agents and integrations that let investment professionals work with governed portfolio data in natural language on a cloud platform built on Snowflake, dbt and AWS. The manager leads a team of contract engineers, writes code most weeks, sets the engineering and evaluation bar, and is accountable for the accuracy, security and reliability of the AI and data services the business depends on. A successful candidate combines strong software and data engineering depth with sound product judgment and a track record of turning ambiguous finance workflows into reliable software, preferably in financial services or private markets.

Responsibilities:

• AI Applications & Agents: Design, build and deploy LLM-powered tools, agents and integrations that let investment, valuation and fund finance teams work with governed portfolio data in natural language. This includes MCP servers and integrations with the firm’s enterprise AI platforms and assistants. Own each solution from discovery through production and adoption.
• AI Evaluation & Quality: Define evaluation cases, regression suites and acceptance criteria for AI features. Measure accuracy, cost and latency, and gate releases on the results. Keep a clear boundary between deterministic calculation and validation on one side and model judgment on the other, so that answers trace back to source data.
• AI Security & Governance: Build controls into the architecture rather than adding them afterward, including OAuth/OIDC authentication, entitlement-based row-level security, SQL and tool guardrails, handling of untrusted content and prompt injection, and audit logging. Partner with Information Security, Compliance and Data Governance.
• Team Leadership: Lead and mentor a team of contract developers, data engineers and QA. Set technical direction, review designs and code, make build-versus-buy recommendations, and stay hands-on in the codebase while fostering collaboration, growth and accountability.
• Data Products & Semantics: Maintain governed, well-documented data models and metadata (metric definitions, glossaries and lineage) so that reporting, applications and AI consumers share a single, trusted source of truth.
• Engineering Practices: Champion code review and automated testing (unit tests, dbt tests, data reconciliation and AI evaluations), along with CI/CD through GitHub Actions, infrastructure as code with Terraform on AWS, and disciplined promotion across development, QA, UAT and production.
• Production Support & Reliability: Own the stability of production AI services and the data pipelines behind them. Lead incident response, root cause analysis and remediation, and put monitoring, alerting and runbooks in place.
• Data Quality & Controls: Ensure accurate, auditable data for valuations, fund performance metrics (such as IRR, MOIC, DPI and NAV) and investor reporting, and support audit and control reviews.
• Platform Modernization: Support the transition of remaining legacy data and reporting workloads to the cloud platform, coordinating with vendor delivery teams.
• AI-Enabled Delivery: Adopt AI coding assistants and agent-based workflows across the team, with human review, safety guardrails and shared team knowledge (rules, skills and documentation), to improve delivery speed and quality.
• Business Partnership & Communication: Work with product owners and business users to turn ambiguous workflows into a prioritized backlog. Run Agile delivery in Jira, and communicate progress, risks, dependencies and technical decisions clearly to senior management and business stakeholders.

Requirements:

Education & Certificates

• Bachelor’s degree, required
• Concentration in computer science, engineering, finance, economics or another quantitative discipline, preferred
• Cloud, data platform or AI certification (for example AWS, Microsoft Azure, SnowPro or dbt), preferred

Professional Experience

• 7+ years of overall relevant experience in software engineering, data engineering or technical analytics, required
• 3+ years building and operating production software or data solutions used by business stakeholders, required
• 2+ years leading engineering delivery, including owning a backlog, setting technical direction and coding standards, and reviewing the work of developers or contractors, required
• Strong proficiency in Python and SQL, plus experience with C#/.NET or TypeScript, required
• Demonstrated experience shipping LLM-powered applications to production users, including tool use and agents, retrieval, structured outputs, and prompt and context design, required
• Experience evaluating AI systems by designing test cases and regression suites, diagnosing failures, tuning against measured results, and validating model output against source data, required
• Hands-on experience with a cloud data platform and modern transformation tooling (for example Snowflake, Databricks or Azure Synapse, with dbt or Spark), required. Snowflake and dbt strongly preferred
• Experience with AWS or Azure, CI/CD (for example GitHub Actions) and infrastructure as code (Terraform), required. AWS experience (ECS, Lambda, S3) is a plus
• Experience with the Model Context Protocol (MCP), agent frameworks, or building tools and integrations for AI assistants, preferred
• Knowledge of data architecture, including dimensional and semantic modeling, layered architectures, metadata and lineage, and data quality and reconciliation techniques, required
• Understanding of security for data and AI systems, including OAuth/OIDC authentication, role- or entitlement-based access to sensitive financial data, and auditability, required
• Experience managing production support, including incident management, root cause analysis and remediation, preferred
• Familiarity with private equity, asset management, transaction advisory or other financial services data, such as fund structures, deals, valuations, portfolio monitoring and performance metrics, strongly preferred
• Experience with Power BI or similar reporting tools, preferred
• Hands-on use of AI coding assistants (for example Claude Code, Cursor or GitHub Copilot) in a team setting, including code review and guardrails for AI-generated work, required
• Strong leadership, mentoring and communication skills, with the ability to turn ambiguous business needs into working software and to work effectively with business teams and contractors

Benefits/Compensation

The compensation range for this role is specific to Washington, D.C, 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.

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.

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Who We Are

When people, ideas, and capital come together, opportunity expands across private markets. At Carlyle, this belief has shaped how we invest for decades, fueling growth for companies and delivering performance for investors.

Visit our website to learn more about our firm and the ways our platform is shaping private markets.