The Lead Engineer, AI & Process Automation sits within Carlyle's Corporate Services Technology organization and is dedicated to driving AI and automation solutions across business domains including Finance, Tax, Human Capital, Legal & Compliance, and Marketing & Communications - partnering directly with function leaders to reimagine how this work gets done. Corporate Services is where AI can compound the firm's operating leverage fastest: high-volume document and data workflows, deep institutional knowledge, and clear measures of throughput and quality. We are building Carlyle's next generation of AI-native products against that opportunity, and we are investing aggressively in the talent, tooling, and platforms required to win.
This is a builder's role at the center of Carlyle's AI and process automation strategy for Corporate Services: a hands on engineering leader who embeds with the business, owns end to end delivery, and operates with full ownership over the outcomes they ship.
You will embed directly with Carlyle's Corporate Services functions - Finance, Tax, Human Capital, Legal & Compliance, Marketing & Communications, and the other enterprise teams that run the firm - to identify the highest leverage AI opportunities and ship them into production, translating ambiguous business problems into working software in weeks, not quarters.
You will build custom AI solutions using modern coding agents and developer tools - Claude Code, Cursor, and the surrounding DevOps stack - to deliver applications, agentic workflows, and copilots tailored to how Corporate Services actually works. Carlyle takes a best of breed approach to AI: you will compose the right models, frameworks, and platforms for each problem rather than committing the firm to a single vendor.
You will also help lead and grow the team, mentoring more junior engineers on the team, raising the technical bar, and shaping how the function delivers software at scale.
What Success Looks Like
In the first 12 months, you will have shipped multiple production AI products embedded in real Corporate Services workflows, established the engineering patterns and reusable building blocks that accelerate every subsequent build, helped grow and develop a team of AI-forward engineers, and earned recognition as a trusted technical partner at the enterprise level. Your work will be visible at the highest levels of the firm.
In-Office Requirement: 4 days a week
Solution Delivery (≈45%)
- 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.
AI Solution Strategy (≈30%)
- 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.
People Management (≈20%)
- 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.
Engineering Community of Practice (≈5%)
- 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.
Education & Certifications
- Bachelor's degree, required
- Concentration in computer science, software engineering, mathematics, physics, data science, or a related technical field, preferred
- Master's degree, preferred
Professional Experience
- 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. Financial services experience preferred but not required.
- Track record of contributing to technical strategy and architectural direction, not only delivery on assigned tasks.
Competencies & Attributes
- 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.
Benefits/Compensation
The compensation range for this role is specific to Washington, DC, and takes into account a wide range of factors including but not limited to the skill sets required/preferred; prior experience and training; licenses and/or certifications.
The anticipated base salary range for this role is $170,000 to $190,000.
In addition to the base salary, the hired professional will enjoy a comprehensive benefits package spanning retirement benefits, health insurance, life insurance and disability, paid time off, paid holidays, family planning benefits and various wellness programs. Additionally, the hired professional may also be eligible to participate in an annual discretionary incentive program, the award of which will be dependent on various factors, including, without limitation, individual and organizational performance.
Due to the high volume of candidates, please be advised that only candidates selected to interview will be contacted by Carlyle.
