Manager, Global Private Equity Data and AI Engineer

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.

Who We Are

Preferred Qualifications

• 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
• 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
• 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
• 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.