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Carlyle

Global private equity and credit investor

Forward Deployed Engineer - Client Solutions Group

AI Application Engineering +1Confirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A bachelor's degree is required.
  • A concentration in computer science, data engineering, information systems, or a related field is preferred.
  • A master's degree is preferred.
  • At least 6 years of overall relevant technical experience is required.
  • Professional software engineering experience with deep expertise in full-stack development, microservices, and system integrations, including experience as a technical lead or architect, is required.
  • Hands-on experience using AI coding agents such as Cursor or Claude Code and applying AI-Driven Development Life Cycle principles and tooling to accelerate delivery teams is required.
  • Working fluency with modern AI platforms and tooling, such as AWS Bedrock, Databricks, Snowflake Cortex, OpenAI or Anthropic APIs, LangChain or LlamaIndex, MLflow, and vector databases such as pgvector or Pinecone, is required.
  • Strong hands-on proficiency with modern frontend frameworks such as React, Angular, or Vue, plus backend microservices and automation in a high-level language or framework, is required.
  • Proven experience building and deploying applications on AWS, including Lambda, Step Functions, API Gateway, S3, CloudWatch, and RDS, is required.
  • Experience with infrastructure-as-code, CI/CD pipelines, containerization such as Docker, feature toggles, automated testing, and incremental production delivery is required.
  • A deep understanding of RESTful APIs, event-driven architectures, and secure data integration, including integrations between web portals and CRM platforms and internal and third-party systems, is required.
  • Prior experience supporting Client Solutions Group, fund reporting, or private equity investor servicing functions is required.
  • Familiarity with data governance, auditability, and compliance, and exposure to business intelligence, reporting, or data visualization tools, is required.
  • Experience scoping and shipping software directly with business users in regulated, high-stakes environments is required.
  • Direct hands-on experience architecting and shipping production generative AI systems, including retrieval, grounding, and semantic layers for LLM- and agent-based applications, RAG architectures, vector stores, embedding strategies, and structured tool use, is preferred but not required.
  • Financial services experience is preferred but not required.

Responsibilities

  • Partner with Client Solutions Group product managers, Finance, and Fund Management stakeholders across GPE, Credit, and AlpInvest to understand user and business needs and document current-state Global Wealth operational processes, calculation logic, and data sources.
  • Translate business processes into clear technical requirements and target-state designs.
  • Facilitate working sessions and process walkthroughs with process owners to validate findings, resolve inconsistencies in metric interpretation, and build stakeholder alignment on standardized definitions.
  • Advise business stakeholders on what automation can realistically deliver and in what sequence.
  • Architect AI-ready data foundations, semantic layers, contextual metadata, data contracts, and retrieval-ready knowledge stores so that LLMs, agents, and generative AI applications can reason reliably over investor and fund data.
  • Design and help build pipelines, calculation logic, and governance controls that automate data processing across the firm's target-state architecture.
  • Implement AWS-native services, including Lambda, Step Functions, ECS, API Gateway, S3, and RDS, to create robust, highly available, scalable platform infrastructure.
  • Use appropriate high-level languages and frameworks for backend microservices, data processing, and serverless orchestration.
  • Prototype and ship automated replacements for manual processes, prioritizing the highest-volume and highest-risk metrics first.
  • Architect solutions for continuous delivery using feature flags, canary deployments, and blue/green environments to reduce risk and accelerate release cycles.
  • Build automated unit, integration, and end-to-end tests to ensure production code quality and reliability.
  • Integrate with CI/CD pipelines, observability tools, and security practices from development through deployment, contributing to a DevOps culture.
  • Establish reusable patterns, data definitions, and calculation templates to accelerate onboarding of each new metric or fund, and maintain end-to-end data lineage from source system through calculation to investor-facing report.
  • Lead architectural design and code reviews, mentor engineers and analysts, uphold engineering standards, and influence technical decisions across a matrixed, federated organization without direct authority.
  • Support and lead a group of senior developers to coordinate delivery of new features and ongoing platform maintenance.
  • Support the design of data quality checks, exception handling, and review workflows to replace manual, ad-hoc reasonability checks.
  • Track and communicate progress to technology leadership and business sponsors, and identify data quality, governance, and control gaps and the requirements to close them.
  • Contribute to change management and adoption efforts as manual processes are retired in favor of automated solutions.

Desired Qualifications

  • Direct hands-on experience architecting and shipping production generative AI systems, including retrieval, grounding, and semantic layers for LLM- and agent-based applications, RAG architectures, vector stores, embedding strategies, and structured tool use.
  • Financial services experience.

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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