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About This Role
Company Profile
The Carlyle Group (NASDAQ: CG) is a global investment firm with $485 billion of assets under management, across 678 investment vehicles as of June 30, 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.
Position Summary:
The Carlyle Group seeks a hands-on engineering manager to lead AI and data engineering for Global Private Equity (GPE). The team builds and runs the platform that brings together portfolio company, deal, fund, valuation and performance data from Carlyle’s investment, portfolio monitoring and enterprise systems, and delivers it to investment teams, valuations, fund finance, investor relations and leadership through reporting, applications and AI-enabled tools. The role designs and ships production AI capabilities, including Model Context Protocol (MCP) services, agents and integrations that let investment professionals work with governed portfolio data in natural language on a cloud platform built on Snowflake, dbt and AWS. The manager leads a team of contract engineers, writes code most weeks, sets the engineering and evaluation bar, and is accountable for the accuracy, security and reliability of the AI and data services the business depends on. A successful candidate combines strong software and data engineering depth with sound product judgment and a track record of turning ambiguous finance workflows into reliable software, preferably in financial services or private markets.
Responsibilities:
- AI Applications & Agents: Design, build and deploy LLM-powered tools, agents and integrations that let investment, valuation and fund finance teams work with governed portfolio data in natural language. This includes MCP servers and integrations with the firm’s enterprise AI platforms and assistants. Own each solution from discovery through production and adoption.
- AI Evaluation & Quality: Define evaluation cases, regression suites and acceptance criteria for AI features. Measure accuracy, cost and latency, and gate releases on the results. Keep a clear boundary between deterministic calculation and validation on one side and model judgment on the other, so that answers trace back to source data.
- AI Security & Governance: Build controls into the architecture rather than adding them afterward, including OAuth/OIDC authentication, entitlement-based row-level security, SQL and tool guardrails, handling of untrusted content and prompt injection, and audit logging. Partner with Information Security, Compliance and Data Governance.
- Team Leadership: Lead and mentor a team of contract developers, data engineers and QA. Set technical direction, review designs and code, make build-versus-buy recommendations, and stay hands-on in the codebase while fostering collaboration, growth and accountability.
- Data Products & Semantics: Maintain governed, well-documented data models and metadata (metric definitions, glossaries and lineage) so that reporting, applications and AI consumers share a single, trusted source of truth.
- Engineering Practices: Champion code review and automated testing (unit tests, dbt tests, data reconciliation and AI evaluations), along with CI/CD through GitHub Actions, infrastructure as code with Terraform on AWS, and disciplined promotion across development, QA, UAT and production.
- Production Support & Reliability: Own the stability of production AI services and the data pipelines behind them. Lead incident response, root cause analysis and remediation, and put monitoring, alerting and runbooks in place.
- Data Quality & Controls: Ensure accurate, auditable data for valuations, fund performance metrics (such as IRR, MOIC, DPI and NAV) and investor reporting, and support audit and control reviews.
- Platform Modernization: Support the transition of remaining legacy data and reporting workloads to the cloud platform, coordinating with vendor delivery teams.
- AI-Enabled Delivery: Adopt AI coding assistants and agent-based workflows across the team, with human review, safety guardrails and shared team knowledge (rules, skills and documentation), to improve delivery speed and quality.
- Business Partnership & Communication: Work with product owners and business users to turn ambiguous workflows into a prioritized backlog. Run Agile delivery in Jira, and communicate progress, risks, dependencies and technical decisions clearly to senior management and business stakeholders.
Requirements:
Education & Certificates
- Bachelor’s degree, required
- Concentration in computer science, engineering, finance, economics or another quantitative discipline, preferred
Cloud, data platform or AI certification (for example AWS, Microsoft Azure, SnowPro or dbt), preferred *
Professional Experience
- 7+ years of overall relevant experience in software engineering, data engineering or technical analytics, required
- 3+ years building and operating production software or data solutions used by business stakeholders, required
- 2+ years leading engineering delivery, including owning a backlog, setting technical direction and coding standards, and reviewing the work of developers or contractors, required
- Strong proficiency in Python and SQL , plus experience with C#/.NET or TypeScript , required
- Demonstrated experience shipping LLM-powered applications to production users, including tool use and agents, retrieval, structured outputs, and prompt and context design, required
- Experience evaluating AI systems by designing test cases and regression suites, diagnosing failures, tuning against measured results, and validating model output against source data, required
- Hands-on experience with a cloud data platform and modern transformation tooling (for example Snowflake , Databricks or Azure Synapse, with dbt or Spark), required. Snowflake and dbt strongly preferred
- Experience with AWS or Azure , CI/CD (for example GitHub Actions ) and infrastructure as code ( Terraform ), required. AWS experience (ECS, Lambda, S3) is a plus
- Experience with the Model Context Protocol (MCP) , agent frameworks, or building tools and integrations for AI assistants, preferred
- Knowledge of data architecture, including dimensional and semantic modeling, layered architectures, metadata and lineage, and data quality and reconciliation techniques, required
- Understanding of security for data and AI systems, including OAuth/OIDC authentication, role- or entitlement-based access to sensitive financial data, and auditability, required
- Experience managing production support, including incident management, root cause analysis and remediation, preferred
- Familiarity with private equity, asset management, transaction advisory or other financial services data, such as fund structures, deals, valuations, portfolio monitoring and performance metrics, strongly preferred
- Experience with Power BI or similar reporting tools, preferred
- Hands-on use of AI coding assistants (for example Claude Code, Cursor or GitHub Copilot) in a team setting, including code review and guardrails for AI-generated work, required
Strong leadership, mentoring and communication skills, with the ability to turn ambiguous business needs into working software and to work effectively with business teams and contractors *
Benefits/Compensation
The compensation range for this role is specific to Washington, D.C, 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.
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.
Who We Are
When people, ideas, and capital come together, opportunity expands across private markets. At Carlyle, this belief has shaped how we invest for decades, fueling growth for companies and delivering performance for investors.
Visit our website to learn more about our firm and the ways our platform is shaping private markets.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
The Carlyle Group AI Hiring
The Carlyle Group has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Washington, DC, US, New York, NY. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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