Lead Engineer, AI & Process Automation

The Carlyle Group•Washington, DC

48d•$170,000 - $190,000•Hybrid

About The Position

The Lead Engineer, AI & Process Automation is a key role within Carlyle’s Corporate Services Technology organization, focused on driving AI and automation solutions across various business domains including Finance, Tax, Human Capital, Legal & Compliance, and Marketing & Communications. This role involves partnering directly with function leaders to innovate and improve operational efficiency through AI and automation. The position is hands-on, requiring the engineer to embed with business functions, own end-to-end delivery of AI-native products, and operate with full accountability for outcomes. The engineer will utilize modern coding agents and developer tools like Claude Code and Cursor, along with a DevOps stack, to build applications, agentic workflows, and copilots. A best-of-breed approach to AI will be employed, selecting the most suitable models, frameworks, and platforms for each problem. The role also includes leadership responsibilities, such as mentoring junior engineers, raising the technical bar, and shaping the function's software delivery at scale. Success in the first 12 months involves shipping multiple AI products, establishing engineering patterns, growing the team, and becoming a trusted technical partner.

Requirements

Bachelor’s degree, required
7+ years of overall relevant hands on engineering experience, required
3+ years building and shipping production AI applications LLM powered apps, agentic workflows, RAG systems, or copilots to real users, required
Fluency with modern AI coding agents and developer tools (e.g., Claude Code, Cursor) and the surrounding DevOps stack version control, CI/CD, testing, containerization, and cloud deployment.
Experience leading and developing engineers, including direct people management or tech lead responsibilities for a team shipping production software.
Deep experience working across both structured data (lakehouse, warehouse, transactional, and time series sources) and unstructured data (PDFs, documents, transcripts, semi structured sources) at scale. Hands on with document intelligence, OCR pipelines, LLM based extraction, and workflow automation that integrates these into end to end business processes.
Direct experience building AI products end to end: agentic workflows, RAG systems, copilots, document intelligence, or autonomous decisioning systems shipped to real users.
Strong coding fundamentals in Python and TypeScript or Java. Comfortable across the stack, from Spark transforms to React front ends.
Experience working directly with business users to scope and ship software in regulated, high stakes environments.
Track record of contributing to technical strategy and architectural direction, not only delivery on assigned tasks.
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.
Leverage mindset. You see every use case as an opportunity to make the next one faster. You build patterns, not snowflakes, and you invest in reusable building blocks that compound across the team’s work.
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.
Engineering leadership. You set technical direction by example, raise the bar through code review and mentorship, and create the conditions for engineers around you to do their best work.
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, software engineering, mathematics, physics, data science, or a related technical field, preferred
Master’s degree, preferred
Financial services experience preferred but not required.

Responsibilities

Embed with business stakeholders to identify, scope, and ship next generation AI products that change how Carlyle works.
Own end to end execution: discovery, requirements, architecture, build, deployment, adoption, and iteration.
Build AI native applications including agentic workflows, LLM powered analytics, document intelligence pipelines, and human in the loop copilots that turn data into decisions.
Automate high-volume Corporate Services processes end to end — from intake and data capture through approvals, exceptions, and downstream systems — retiring manual work and freeing teams to focus on higher-value judgment.
Move at the speed required to keep AI at the leading edge: prototype in days, harden in weeks, and operate at firm scale.
Partner deeply with users in their environment so that what you ship is what they actually use, not what they asked for in a kickoff meeting.
Shape the technical strategy, roadmap, and architectural direction for AI across Corporate Services, prioritizing the use cases with the highest impact on the firm.
Define and enforce the standards, patterns, and guardrails that govern how AI solutions are built, balancing speed of delivery with long term maintainability, security, and responsible AI practices.
Evaluate and select the right models, frameworks, and platforms for each problem — commercial LLMs, open source models, agentic frameworks, and Carlyle’s broader data and infrastructure stack — in line with the firm’s best of breed approach.
Establish reusable building blocks (component libraries, evaluation frameworks, prompt and agent patterns, deployment templates) so each new use case starts further down the field than the last.
Partner with infrastructure, data, and security teams to ensure AI solutions deploy cleanly into Carlyle’s environment and meet enterprise standards for observability, controls, and audit.
Monitor the AI landscape and bring promising tools, models, and techniques into the firm’s practice as they mature.
Lead and develop a team of more junior AI-forward engineers, setting direction, allocating work, and owning their growth and performance.
Mentor and coach engineers on AI and automation engineering craft — working effectively with coding agents and modern developer tools, designing workflows that combine AI with deterministic automation, evaluating model outputs, designing for reliability, and operating production systems with confidence.
Run a high standard for code quality and engineering practice: code review, testing, deployment hygiene, monitoring, and incremental hardening of high stakes systems.
Attract, hire, and retain top AI engineering talent, and build a team culture where the best engineers want to work.
Foster a build-and-ship culture grounded in customer obsession, fast iteration, and intellectual honesty about what AI can and cannot do today.
Represent Corporate Services Technology in Carlyle’s Engineering Community of Practice, sharing wins, lessons learned, reusable patterns, and post mortems with engineering peers across the firm.
Learn from counterparts supporting the investment segments and other business areas to surface acceleration opportunities, avoid duplicate work, and bring proven approaches back into the Corporate Services portfolio.
Contribute to firm wide engineering standards, tooling decisions, and AI practices so the function both shapes and benefits from the broader engineering culture at Carlyle.