Manager, Global Private Equity Data and AI Engineer

Lead hands-on delivery of production AI and governed data services for Global Private Equity, supporting investment professionals with natural-language access to trusted portfolio data.

Responsibilities

• Design, build and deploy LLM-powered tools, agents and integrations for investment, valuation and fund finance teams to work with governed portfolio data in natural language.
• Develop solutions that include Model Context Protocol (MCP) servers and integrations with the firm’s enterprise AI platforms and assistants, owning each initiative end-to-end from discovery through production and adoption.
• Define evaluation cases, regression suites and acceptance criteria for AI features; track accuracy, cost and latency; gate releases on results.
• Establish architectural boundaries between deterministic calculation and validation versus model judgment, ensuring answers trace back to source data.
• Implement controls for governed access and safety, including OAuth/OIDC authentication, entitlement-based row-level security, SQL and tool guardrails, handling of untrusted content and prompt injection, plus audit logging.
• Partner with Information Security, Compliance and Data Governance to support secure and auditable AI and data services.
• 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 remain hands-on in the codebase.
• Maintain governed data models and metadata, including metric definitions, glossaries and lineage, to support a single source of truth across reporting, applications and AI consumers.
• Promote code review and automated testing across unit tests, dbt tests, data reconciliation and AI evaluations.
• Operate CI/CD using GitHub Actions, apply infrastructure as code with Terraform on AWS, and manage disciplined promotion across development, QA, UAT and production.
• Own production stability for AI services and the underlying data pipelines, including incident response, root cause analysis and remediation, with monitoring, alerting and runbooks.
• Ensure accurate, auditable data for valuations and performance metrics (including IRR, MOIC, DPI and NAV) and investor reporting; support audit and control reviews.
• Support transition of remaining legacy data and reporting workloads to the cloud platform, coordinating with vendor delivery teams.
• Adopt AI coding assistants and agent-based workflows with human review and safety guardrails, sharing team knowledge (rules, skills and documentation) to improve delivery speed and quality.
• Work with product owners and business users to convert ambiguous workflows into a prioritized backlog; deliver using Agile in Jira; communicate progress, risks, dependencies and technical decisions to senior management and stakeholders.

Requirements

• Bachelor’s degree (required).
• Concentration in computer science, engineering, finance, economics or another quantitative discipline (preferred).
• Cloud, data platform or AI certification (examples: AWS, Microsoft Azure, SnowPro, dbt) (preferred).
• 7+ years 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 developer or contractor work (required).
• Strong Python and SQL proficiency, plus experience with C#/.NET or TypeScript (required).
• Demonstrated experience shipping LLM-powered applications to production users, including tool use/agents, retrieval, structured outputs, and prompt/context design (required).
• Experience evaluating AI systems by designing test cases and regression suites, diagnosing failures, tuning against measured results, and validating outputs against source data (required).
• Hands-on experience with a cloud data platform and modern transformation tooling (examples: Snowflake, Databricks, Azure Synapse with dbt or Spark); Snowflake and dbt strongly preferred (required).
• Experience with AWS or Azure, CI/CD (example: GitHub Actions) and infrastructure as code (Terraform) (required). AWS experience (ECS, Lambda, S3) is a plus.
• Experience with 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/lineage, and data quality/reconciliation (required).
• Understanding of security for data and AI systems: OAuth/OIDC, role/entitlement-based access to sensitive financial data, and auditability (required).
• Production support experience (incident management, root cause analysis, remediation) (preferred).
• Familiarity with private equity, asset management, transaction advisory or other financial services data (strongly preferred).
• Experience with Power BI or similar reporting tools (preferred).
• Hands-on use of AI coding assistants (examples: Claude Code, Cursor, GitHub Copilot) in a team setting, including code review and guardrails for AI-generated work (required).
• Strong leadership, mentoring and communication skills, including turning ambiguous needs into working software and collaborating with business teams and contractors (required).

Technologies

• Python, SQL, C#/.NET, TypeScript
• LLM-powered applications; Model Context Protocol (MCP) and MCP servers
• Snowflake, dbt, Databricks, Azure Synapse
• AWS (ECS, Lambda, S3), Azure
• CI/CD (GitHub Actions), Terraform, infrastructure as code
• OAuth/OIDC, row-level security
• Jira, Power BI
• Claude Code, Cursor, GitHub Copilot
• Unit tests, data reconciliation, AI evaluations

Benefits

• Retirement benefits
• Health insurance
• Life insurance and disability
• Paid time off
• Paid holidays
• Family planning benefits
• Various wellness programs
• Annual discretionary incentive program (eligibility)

Company profile

• 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.
• Invests across Global Private Equity, Global Credit and Carlyle AlpInvest.

Position summary

• Builds and runs a platform that brings together portfolio company, deal, fund, valuation and performance data from Carlyle’s investment, portfolio monitoring and enterprise systems.
• Delivers data to investment teams, valuations, fund finance, investor relations and leadership through reporting, applications and AI-enabled tools.
• Designs and ships production AI capabilities, including MCP services, agents and integrations for governed portfolio data on a cloud platform built on Snowflake, dbt and AWS.
• Manager leads a team of contract engineers, writes code most weeks, sets the engineering and evaluation bar, and is accountable for accuracy, security and reliability of AI and data services.

Compensation

• Compensation range is specific to Washington, D.C.
• Comprehensive benefits package includes retirement benefits, health insurance, life insurance and disability, paid time off, paid holidays, family planning benefits and various wellness programs.