Manager, Global Private Equity Data and AI Engineer

The Carlyle Group•Washington, DC

19d•Hybrid

About The Position

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.

Requirements

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
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

Nice To Haves

Concentration in computer science, engineering, finance, economics or another quantitative discipline
Cloud, data platform or AI certification (for example AWS, Microsoft Azure, SnowPro or dbt)
Experience with the Model Context Protocol (MCP), agent frameworks, or building tools and integrations for AI assistants
Experience managing production support, including incident management, root cause analysis and remediation
Familiarity with private equity, asset management, transaction advisory or other financial services data, such as fund structures, deals, valuations, portfolio monitoring and performance metrics
Experience with Power BI or similar reporting tools
AWS experience (ECS, Lambda, S3)

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.