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About This Role
### Basic information
Job Name:
Engineer, AI \& Process Automation
Location:
Washington, DC
Line of Business:
Global Technology \& Solutions
Job Function:
Investor Services
Date:
Wednesday, June 3, 2026
### Position Summary
The Engineer, AI \& Process Automation sits within Carlyle’s Corporate Services Technology organization, reporting to the Lead Engineer, AI \& Process Automation, and contributes hands\-on to the team’s mission of delivering AI and automation solutions across business domains including Finance, Tax, Human Capital, Legal \& Compliance, and Marketing \& Communications. 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 on the team driving Carlyle’s AI and process automation work for Corporate Services: a hands on engineer who embeds with the business, owns delivery on the products they build, and operates with full accountability for 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 — working with your manager and team to take the highest leverage AI opportunities from idea to production, and 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, and you will work within that approach — reaching for the right models, frameworks, and tools for each problem rather than defaulting to a single vendor.
You will contribute to engineering quality across the team — sharing patterns, reviewing each other’s code, and helping reusable building blocks compound across the work the team ships.
What Success Looks LikeIn the first 12 months, you will have shipped multiple production AI products embedded in real Corporate Services workflows, contributed reusable patterns and building blocks that accelerate the team’s subsequent work, and earned a reputation as a trusted technical partner to the business stakeholders you serve. Your work will be visible at senior levels of the firm.
In\-Office Requirement: 4 days a week
### Responsibilities
Solution Delivery ( 75%)* Embed with business stakeholders to scope and ship AI and automation products that change how Carlyle works.
- Own delivery on the products you build: discovery, requirements, build, deployment, adoption, and iteration, in partnership with your manager and the rest of the team.
- 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.
Engineering Craft \& Reusability ( 25%)* Write production quality code and apply rigorous engineering practice: code review, testing, deployment hygiene, monitoring, and incremental hardening of high stakes systems.
- Use modern AI coding agents and developer tools (Claude Code, Cursor) and the surrounding DevOps stack effectively to deliver faster without sacrificing quality.
- Build within the team’s standards, patterns, and guardrails for AI solutions, and contribute back improvements as you discover them.
- Contribute reusable building blocks (components, evaluation harnesses, 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 what you ship deploys cleanly into Carlyle’s environment and meets enterprise standards for observability, controls, and audit.
- Stay current on the AI landscape and bring promising tools, models, and techniques to your manager and the team for evaluation.
### Qualifications
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* 5\+ years of overall relevant hands on engineering experience, required
- 2\+ 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.
- 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.
- 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.
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.
- 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.
- Strong collaborator. You work well with business stakeholders and engineering peers, ask the right questions, communicate trade\-offs clearly, and bring people along on the choices you make.
- Curious and self\-directed. You look beyond assigned tasks to spot improvements, suggest alternatives, and contribute to how the team builds.
- 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/CompensationThe 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 $160,000 to $180,000\.
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.
### Company Information
The Carlyle Group (NASDAQ: CG) is a global investment firm with $475 billion of assets under management, across 678 investment vehicles as of March 31, 2026\. Founded in 1987 in Washington, DC, Carlyle has grown into one of the world's largest and most successful investment firms, with more than 2,500 professionals operating in 28 offices in North America, Europe, the Middle East, Asia and Australia.
Carlyle’s purpose is to connect people, ideas, and capital to fuel growth for companies and performance for investors, which range from public and private pension funds to wealthy individuals and families to sovereign wealth funds, unions and corporations. Carlyle invests across three segments – Global Private Equity, Global Credit and Carlyle AlpInvest – and has deep expertise across industries, markets, and geographies.
At Carlyle, we believe that a wide spectrum of experiences and viewpoints drives performance and success. Our CEO, Harvey Schwartz, has stated that, "To build better businesses and create value for all of our stakeholders, we are focused on assembling leadership teams with the strongest insights from a range of perspectives." Reflecting this view, emphasis is placed on development, retention and inclusion through our internal processes and seven Employee Resource Groups (ERGs). We cultivate a culture where ideas are openly shared and challenged, connecting diverse expertise and perspectives to drive enduring value.
Salary Context
This $160K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At The Carlyle Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($170K) sits 5% below the category median. Disclosed range: $160K to $180K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
The Carlyle Group AI Hiring
The Carlyle Group has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $180K - $190K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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