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Carlyle

Global private equity and credit investor

Lead Forward Deployed Product Engineer

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeWashington, DC, USA

About the Job

Requirements

  • A bachelor's degree is required.
  • At least 10 years of relevant software engineering experience with increasing technical scope and ownership is required.
  • Demonstrated history of owning business-critical applications end-to-end and delivering measurable business outcomes is required.
  • Hands-on experience building production systems using Python, with strong experience across enterprise data platforms and databases such as Snowflake, Databricks, PostgreSQL, or Microsoft SQL Server.
  • Frontend development experience with React and/or Next.js.
  • Experience developing or integrating production AI-enabled applications, including large language models, retrieval-augmented generation pipelines, and orchestration frameworks such as LangChain or LlamaIndex.
  • Full-stack application development using Python, React, and/or Next.js.
  • API design and service-oriented architecture.
  • Modern data platforms, relational databases, and data pipeline design.
  • Applied AI and large language model integration, including retrieval-augmented generation, agentic workflows, and prompt design.
  • Technical leadership, mentorship, and ability to set and enforce engineering standards across a team.
  • Ability to work directly with business users in a fast-paced, delivery-focused environment and drive outcomes in ambiguous problem spaces.
  • Executive presence and written and verbal communication skills.

Responsibilities

  • Own a portfolio of business-critical Global Private Equity 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 and/or Next.js on the frontend, working directly with the business.
  • Develop scalable APIs, services, and data pipelines integrating the enterprise data platform Snowflake, third-party market and portfolio data, and internal systems.
  • Prototype rapidly against real data to validate use cases, then harden successful prototypes into secure, production-grade software.
  • Maintain high standards for code quality, performance, security, and maintainability.
  • Embed with Deal Teams and Fund Management to learn workflows and identify high-value problems.
  • Translate ambiguous business needs into technical designs and shippable deliverables while partnering with senior investment and fund management stakeholders.
  • Drive adoption through hands-on enablement, demonstrations, and feedback loops, measuring usage and business impact.
  • Represent Global Private Equity Technology to senior stakeholders, including deal and fund management leadership.
  • Build and integrate AI-enabled functionality, including large language model copilots, intelligent automation, and agentic workflows, where it provides measurable value across the investment lifecycle.
  • Develop retrieval-augmented generation pipelines and large language model workflows using orchestration frameworks and enterprise large language model platforms.
  • Design secure, governed patterns for connecting large language models to proprietary data, ensuring production readiness, permissions, and scalability.
  • 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 for the broader Product and Engineering organization.
  • Mentor and provide technical guidance to engineers, product managers, and analysts.
  • Provide design input, code review, and delivery oversight to onshore and offshore development teams.
  • Align delivery across shared platforms and roadmaps with the Head of Global Private Equity Engineering and product management peers.
  • Contribute to the Global Private Equity Technology product and applied-AI roadmap and help prioritize engineering investment.
  • Ensure solutions comply with AI governance, data privacy, and information security standards.
  • Communicate delivery status, technical trade-offs, and business value to senior technology and business leadership.
  • Create and maintain technical documentation and architectural artifacts, and share reusable components and learnings across the technology organization.

Desired Qualifications

  • A concentration in computer science, engineering, or a related quantitative field is preferred.
  • A master's degree is preferred.
  • Familiarity with enterprise large language model platforms such as AWS Bedrock or Anthropic Claude is preferred.
  • Experience partnering directly with business or investment users in an embedded or forward-deployed delivery model is strongly preferred.
  • Experience providing technical leadership to engineering teams, including coordinating onshore and offshore delivery, is preferred.
  • Prior experience in alternative asset management, private equity, or financial services is preferred.

About the Company

Carlyle

Global private equity and credit investor

Carlyle manages alternative investments for institutions and high-net-worth individuals. Client money is pooled into funds or managed accounts, and investment teams source, diligence, and actively manage holdings to buy, grow, and exit investments, with returns coming from asset appreciation, income, and realized exits. It differentiates itself with a large global platform across multiple strategies and geographies, broad deal flow, specialized teams, and long-standing relationships that help build diversified portfolios in private equity, credit, and real assets. Its goal is to generate attractive, risk-adjusted returns for clients over the long term while preserving capital and providing liquidity options suitable to each strategy.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1987

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