Forward Deployed Product Engineer

Summary The Forward Deployed Product Engineer, Global Private Equity Technology is a senior, hands-on engineering role responsible for building and scaling the business-critical applications and applied-AI capabilities that power Carlyle’s Global Private Equity (GPE) platform. Operating in a forward-deployed model, this Vice President embeds directly with Deal Teams and Fund Management—working alongside the business, learning its workflows firsthand, and shipping working software that measurably improves how deals are sourced, diligenced, executed, monitored, and grown. This is a builder’s leadership role. The Vice President owns a portfolio of products end-to-end—across backend services, modern frontends, data pipelines, and LLM-based workflows—and sets the technical direction, engineering standards, and delivery patterns that the broader GPE Product and Engineering organization builds on. The ideal candidate pairs deep, current technical execution with sharp product judgment and executive presence, thrives in ambiguous problem spaces, and is energized by turning Carlyle’s proprietary data and domain expertise into scalable, workflow-native capabilities. Unlike a traditional application engineer, the Forward Deployed Product Engineer is measured by business outcomes rather than tickets closed: identifying the highest-value problems, prototyping quickly against real data, hardening what works into production, and driving adoption directly with the investment professionals who use it. In-Office Requirement: 4 days per week Primary Responsibilities Forward-Deployed Delivery & Product Ownership (35%) Own a portfolio of business-critical GPE applications end-to-end—from problem discovery and design through implementation, deployment, adoption, and ongoing support. Design, build, and maintain full-stack applications using Python on the backend and React / Next.js on the frontend, embedded directly with the business rather than working behind a requirements hand-off. Develop scalable APIs, services, and data pipelines that integrate GPE’s enterprise data platform (Snowflake), third-party market and portfolio data (e.g., Chronograph, FactSet, PitchBook), and internal systems. Prototype rapidly against real data to validate use cases in days, then harden winning prototypes into secure, production-grade software. Set and uphold a high bar for code quality, performance, security, and maintainability. Business Partnership & Discovery (20%) Embed with GPE Deal Teams and Fund Management to learn their workflows firsthand and surface the highest-value problems worth solving. Serve as a trusted technical partner to senior investment and fund management stakeholders, translating ambiguous business needs into clear technical designs and shippable deliverables. Drive adoption directly with end users through hands-on enablement, demos, and tight feedback loops—measuring success by usage and business impact, not features shipped. Represent GPE Technology credibly in front of senior stakeholders, including deal and fund management leadership. Applied AI & Intelligent Systems (20%) Build and integrate AI-enabled functionality—LLM copilots, intelligent automation, and agentic workflows—where it delivers clear, measurable value across the investment lifecycle. Develop retrieval-augmented generation (RAG) pipelines and LLM-based workflows using modern orchestration frameworks (e.g., LangChain, LlamaIndex) and enterprise LLM platforms (e.g., AWS Bedrock, Anthropic Claude). Design secure, governed patterns for connecting LLMs to proprietary data (e.g., Snowflake accessed via MCP or equivalent), ensuring solutions are production-ready, permissioned, and scalable. Partner with data, platform, and engineering teams to move applied-AI capabilities from proof-of-concept to production. Technical Leadership & Standards (15%) Set technical direction, architectural patterns, and reusable components that the broader GPE Product and