Forward Deployed Engineer, Client Solutions Group

The Carlyle Group•Washington, DC

62d•$160,000 - $190,000•Hybrid

About The Position

This is a senior engineering role at the center of Carlyle’s AI strategy, modeled on the Forward Deployed Engineer archetype: hands-on technical leaders who embed with the business, own end-to-end delivery, and operate with full ownership over the outcomes they ship. As a senior individual contributor and architectural authority at the intersection of full-stack engineering and applied AI, you will set the technical direction that others build against, partnering across a federated operating model of domain and platform teams. You will embed directly with Client Solutions Group, Finance, and Fund Management teams across GPE, Credit, and AlpInvest to identify the highest-leverage AI and automation opportunities across Global Wealth operational processes, including NAV reporting, fee models, and financial reporting and ship them into production, translating ambiguous business problems into working software in weeks, not quarters. You will design, evolve, and deliver full-stack solutions, and the AI-ready data foundations, semantic layers, and retrieval patterns beneath them, that turn manual, spreadsheet-based processes into automated, standardized, and auditable technology that both people and AI agents can rely on. This position requires someone equally comfortable in a room with Finance and Fund Management stakeholders dissecting a calculation methodology, and at a whiteboard or keyboard designing the data model, semantic layer, pipeline, platform logic, and user-facing application that will replace it, end to end, full stack. What Success Looks Like In the first 12 months, you will have shipped multiple production AI products embedded in real Carlyle workflows, helped define the target-state architecture for AI-ready Global Wealth operational processes and established reusable patterns for retrieval and semantic access that accelerate every initiative that follows, and earned recognition as a trusted technical authority at the enterprise level. Your work will be visible at the highest levels of the firm. In-office requirement: 4 days per week

Requirements

Bachelor’s degree, required
Concentration in computer science, data engineering, information systems, or a related field, preferred
Master’s degree, preferred
6+ years of overall relevant technical experience, required
Professional software engineering experience, with deep expertise in full stack development, microservices, and system integrations, including time spent as a technical lead or architect, required
Proven, hands-on experience using AI coding agents (e.g., Cursor, Claude Code) and applying AI-Driven Development Life Cycle (AI-DLC) principles and tooling to accelerate delivery teams.
Direct, hands-on experience architecting and shipping generative AI systems in production, including designing retrieval, grounding, and semantic layers for LLM- and agent-based applications (RAG architectures, vector stores, embedding strategies, and structured tool use) preferred but not required.
Working fluency with modern AI platforms and tooling — e.g., AWS Bedrock, Databricks, Snowflake Cortex, OpenAI/Anthropic APIs, LangChain/LlamaIndex, MLflow, and vector databases (pgvector, Pinecone, or equivalents) — with the ability to evaluate alternatives.
Strong hands-on proficiency across the stack: modern frontend frameworks (React, Angular, or Vue) plus backend microservices and automation in a high-level language/framework.
Proven experience building and deploying applications on AWS (Lambda, Step Functions, API Gateway, S3, CloudWatch, RDS).
Strong grounding in modern DevOps and delivery practices: infrastructure-as-code, CI/CD pipelines, containerization (e.g., Docker), feature toggles, automated testing, and incremental delivery in production.
Deep understanding of RESTful APIs, event-driven architectures, and secure data integration, including integrating web portals and CRM platforms with internal and third-party systems.
Prior experience supporting Client Solutions Group, fund reporting, or private equity investor servicing functions.
Familiarity with data governance, auditability, and compliance, and exposure to BI/reporting or data visualization tools.
Experience scoping and shipping software directly with business users in regulated, high-stakes environments; financial services experience preferred but not required.
Builder’s instinct under ambiguity. You start by shipping, measure progress in working software not slides, and can turn a vague business problem into a working prototype in a week.
Customer obsession. You sit with users, reimagine workflows alongside them, and ship solutions that are functional in the real world rather than theoretical on a slide.
Platform mindset. You see every use case as an opportunity to make the next one faster. You build patterns, not snowflakes.
Executive presence. You can sit across from a senior leader, ask the right questions, push back when needed, and earn trust.
Intellectual honesty about AI. You know what current models can and cannot do, you design around their limits, and you do not confuse demo magic with production reliability.
AI-forward instinct. You default to asking how AI changes a design, not whether AI can be bolted on later, and you have a clear point of view on where current architectural patterns — agents, tool use, evaluations, guardrails, and observability — actually apply.
Architectural judgment. You translate strategy into executable technical designs, and you can distinguish durable architectural decisions from AI hype.
Hunger to operate at the frontier. You want to build things that have never been built before, at a firm where the work matters.

Nice To Haves

Concentration in computer science, data engineering, information systems, or a related field, preferred
Master’s degree, preferred
Direct, hands-on experience architecting and shipping generative AI systems in production, including designing retrieval, grounding, and semantic layers for LLM- and agent-based applications (RAG architectures, vector stores, embedding strategies, and structured tool use) preferred but not required.
Financial services experience preferred but not required.

Responsibilities

Partner directly 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.
Act as a trusted advisor to 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 the pipelines, calculation logic, and governance controls that automate data processing across the firm's target-state architecture
Implement AWS-native services (Lambda, Step Functions, ECS, API Gateway, S3, and RDS) to create a robust technology infrastructure that supports high availability and scalability on the platform.
Leverage appropriate high-level languages and frameworks for backend microservices, data processing, and serverless orchestration.
Work hands-on to prototype and ship automated replacements for manual processes, prioritizing the highest-volume, highest-risk metrics first.
Architect solutions to support continuous delivery, using techniques like 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 code quality and reliability in production and contribute to a DevOps culture by integrating with CI/CD pipelines, observability tools, and security practices from development through deployment.
Establish reusable patterns, data definitions, and calculation templates so that each newly onboarded metric or fund is faster to deliver than the last 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 across the team, and influence technical decisions across a matrixed, federated organization without direct authority.
Support and lead a group of senior developer to coordinate overall 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 along with the requirements to close them.
Contribute to change management and adoption efforts as manual processes are retired in favor of automated solutions.