Forward Deployed Product Engineer

The Carlyle Group•Washington, DC

65d•$210,000 - $220,000•Hybrid

About The Position

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.

Requirements

Bachelor’s degree, required.
Concentration in computer science, engineering, or a related quantitative field, preferred.
Master’s degree, preferred.
Minimum of 10 years of relevant software engineering experience, required, with a track record of increasing technical scope and ownership.
Demonstrated history of owning business-critical applications end-to-end and delivering measurable business outcomes, required.
Hands-on experience building production systems using Python, with strong experience across enterprise data platforms and databases (e.g., Snowflake, Databricks, PostgreSQL, MS SQL).
Frontend development experience with React and/or Next.js.
Experience developing or integrating production AI-enabled applications, including LLMs, RAG pipelines, and orchestration frameworks (e.g., LangChain, LlamaIndex); familiarity with enterprise LLM platforms such as AWS Bedrock or Anthropic Claude, preferred.
Experience partnering directly with business or investment users in an embedded / forward-deployed delivery model, strongly preferred.
Experience providing technical leadership to engineering teams, including coordinating onshore and offshore delivery, preferred.
Prior experience in alternative asset management, private equity, or financial services, preferred.
Full-stack application development (Python; React / Next.js).
API design and service-oriented architecture.
Modern data platforms, relational databases, and data pipeline design.
Applied AI and LLM integration (RAG, agentic workflows, prompt design).
Strong product judgment and comfort working directly with business users in a fast-paced, delivery-focused environment.
Technical leadership, mentorship, and the ability to set and enforce engineering standards across a team.
Executive presence with excellent written and verbal communication.
Ability to operate autonomously and drive outcomes in ambiguous problem spaces.

Nice To Haves

Concentration in computer science, engineering, or a related quantitative field.
Master’s degree.
Experience developing or integrating production AI-enabled applications, including LLMs, RAG pipelines, and orchestration frameworks (e.g., LangChain, LlamaIndex); familiarity with enterprise LLM platforms such as AWS Bedrock or Anthropic Claude.
Experience partnering directly with business or investment users in an embedded / forward-deployed delivery model.
Experience providing technical leadership to engineering teams, including coordinating onshore and offshore delivery.
Prior experience in alternative asset management, private equity, or financial services.

Responsibilities

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.
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.
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.
Set technical direction, architectural patterns, and reusable components that the broader GPE Product and Engineering organization builds on.
Mentor and provide technical guidance to engineers, product managers, and analysts across the team, raising the overall engineering bar.
Provide design input, code review, and delivery oversight to onshore and offshore development teams.
Partner with the Head of GPE Engineering and product management peers to align delivery across shared platforms and roadmaps.
Contribute to the GPE Technology product and applied-AI roadmap, helping prioritize where engineering investment creates the most value.
Ensure all solutions comply with Carlyle’s AI governance, data privacy, and information security standards.
Communicate delivery status, technical trade-offs, and business value clearly to senior technology and business leadership.
Create and maintain clear technical documentation and architectural artifacts, and share reusable components and learnings across Carlyle Technology.