Forward Deployed Product Engineer — AI for Private Equity

1P284 THE CARLYLE GROUP EMPLOYEE CO., LLC; Washington (District of Columbia); On-site; USD 210,000 - 220,000; Full time

Job summary

The Forward Deployed Product Engineer, Global Private Equity Technology at 1P284 THE CARLYLE GROUP EMPLOYEE CO., LLC is a senior, hands-on engineering leader responsible for building and scaling mission-critical applications and AI-enabled workflows within Carlyle’s GPE platform.

This VP embeds with deal teams and fund management, delivering end-to-end products, setting technical direction, mentoring engineers, and driving adoption of production-grade software across data platforms and LLM-based

Qualifications

• Bachelor’s degree required, concentration in CS/engineering or related quantitative field.
• Master’s degree preferred.

Responsibilities

• Forward-Deployed Delivery & Product Ownership: Own a portfolio of GPE apps end-to-end from discovery to adoption.
• Business Partnership & Discovery: Embed with Deal Teams to translate needs into technical designs and deliverables.
• Applied AI & Intelligent Systems: Build AI-enabled functionality including LLM copilots and RAG pipelines.
• Technical Leadership & Standards: Set patterns, mentor engineers, oversee delivery across teams.
• Strategy, Governance & Communication: Contribute to roadmap and ensure governance and clear documentation.

Skills

Full-stack development

API design

Data platforms & pipelines

Applied AI & LLM integration

Executive presence & communication

Technical leadership

Education

Bachelor's degree

Master's degree

Tools

Snowflake

Databricks

PostgreSQL

MS SQL

AWS Bedrock

Anthropic Claude

Job description

The Forward Deployed Product Engineer, Global Private Equity Technology at 1P284 THE CARLYLE GROUP EMPLOYEE CO., LLC is a senior, hands-on engineering leader responsible for building and scaling mission-critical applications and AI-enabled workflows within Carlyle’s GPE platform.

This VP embeds with deal teams and fund management, delivering end-to-end products, setting technical direction, mentoring engineers, and driving adoption of production-grade software across data platforms and LLM-based