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About This Role
### Basic information
Job Name:
Data \& AI Engineer
Location:
Washington, DC
Line of Business:
Global Technology \& Solutions
Job Function:
Investor Services
Date:
Thursday, May 28, 2026
### Position Summary
Fund or Department DescriptionThe Data \& AI Engineer sits within Carlyle’s Enterprise Technology \& Data organization and supports firm\-wide data and AI initiatives spanning investment platforms, portfolio operations, investor relations, and corporate functions. The role operates within a federated data operating model, partnering with domain engineering teams to implement shared platforms and reusable patterns for data and AI under the technical direction of the Senior AI \& Data Architect.
Position SummaryThe Data \& AI Engineer is an experienced, hands\-on engineer who turns Carlyle’s data and AI architecture into working production systems. Reporting to the Senior AI \& Data Architect, this role is responsible for building and operating the pipelines, semantic layers, retrieval systems, and AI\-ready data products that power analytics, automation, LLMs, agents, and generative AI applications across the firm.
The role requires deep, hands\-on expertise across modern data engineering and applied AI engineering. The Data \& AI Engineer will implement retrieval\-augmented generation (RAG) patterns, embedding and indexing pipelines, vector stores, and semantic models alongside core ELT, streaming, and analytical pipelines — treating LLMs, agents, and copilots as first\-class consumers of the data platform.
This is a senior individual\-contributor engineering role that executes against architectural standards, contributes to their evolution through hands\-on learning, and partners closely with data science, AI engineering, governance, and domain teams to deliver trusted, AI\-consumable data at enterprise scale.
What Success Looks Like: In the first 12 months, this role will deliver foundational AI\-ready data pipelines and retrieval components defined in the target\-state architecture, productionize one or more priority RAG or agent\-grounding use cases, and establish reusable engineering patterns that other domain teams can adopt across the federated data platform.
In\-office requirement: 4 days per week
Location: Washington, D.C. or New York, NY
### Responsibilities
AI Data Pipelines \& Retrieval Systems ( 35%)* Build and operate AI\-ready data pipelines — embedding generation, chunking, indexing, and refresh workflows — that make Carlyle’s enterprise data reliably retrievable by LLMs, agents, and generative AI applications.
- Implement retrieval\-augmented generation (RAG) components, including vector store integrations, hybrid search, re\-ranking, and grounding logic, against architectural patterns defined by the Senior AI \& Data Architect.
- Develop and maintain tool and function interfaces that allow agents and copilots to query and act on enterprise data safely, with appropriate guardrails, logging, and evaluation hooks.
- Partner with Data Science and AI Engineering teams to operationalize feature stores, evaluation datasets, and reusable AI data products.
- Contribute to semantic and context engineering work that powers natural\-language analytics, conversational reporting, and AI\-driven insights for business users.
Modern Data Pipeline Engineering ( 30%)* Design, build, and maintain production\-grade ELT, streaming, and transformation pipelines using tools such as dbt, Fivetran and Snowflake.
- Implement ingestion, modeling, and consumption patterns that meet enterprise standards for scalability, performance, security, resiliency, and cost efficiency.
- Write clean, well\-tested Python and SQL; apply software engineering best practices including version control, code review, CI/CD, modular design, and automated testing.
- Productionize new sources and domains under the federated operating model, partnering with domain data engineers to apply shared platform capabilities consistently.
Semantic Layer \& Data Product Development ( 20%)* Implement semantic models, data contracts, and analytical/dimensional models that enable trusted self\-service analytics and reliable AI grounding.
- Build and maintain reusable data products with clear ownership, documented contracts, and contextual metadata suitable for both human and AI consumers.
- Collaborate with the Senior AI \& Data Architect to refine and extend enterprise semantic standards based on what works in production.
- Support discovery and consumption tooling so that analysts, applications, and agents can find and use data products with minimal friction.
Data Quality, Observability \& AI Trust ( 10%)* Implement data quality checks, lineage capture, and pipeline observability across both data and AI workloads.
- Build logging, evaluation, and monitoring components for AI systems — including prompt and response capture, retrieval metrics, and model performance signals — in line with governance standards.
- Partner with Data Governance to operationalize metadata, stewardship, and access controls, ensuring AI systems consume enterprise data with the same rigor as human users.
- Surface issues early, propose remediations, and feed lessons learned back into architectural patterns.
Collaboration \& Engineering Craft ( 5%)* Participate in architectural design reviews and contribute hands\-on engineering perspective to evolving patterns and standards.
- Mentor junior data engineers and analysts on modern data and AI engineering practices.
- Document patterns, write runbooks, and share knowledge across the federated organization to accelerate adoption of reusable platform capabilities.
### Qualifications
Education \& Certifications* Bachelor’s degree, required
- Concentration in computer science, data engineering, information systems, or a related field, preferred
- Masters degree, preferred
- Relevant certifications in cloud, data engineering, analytics, or AI/ML are preferred
Professional Experience* 6\+ years of overall relevant technical experience, required
- Experience in data engineering, analytics engineering, or platform engineering, with at least 1–2 years of direct, hands\-on experience building generative AI or AI/ML systems in production.
- Proven experience implementing retrieval, grounding, and semantic components for LLM\- or agent\-based applications, including RAG pipelines, vector stores, embedding workflows, and structured tool use.
- Hands\-on experience with one or more modern AI platforms and tooling categories (e.g., AWS Bedrock, Databricks ML, Snowflake Cortex, OpenAI/Anthropic APIs, LangChain/LlamaIndex or equivalents, MLflow, and vector databases such as Databricks Vector Search, pgvector, or Pinecone).
- Strong, demonstrable expertise in Python and SQL, with working knowledge of distributed processing frameworks (e.g., Spark).
- Deep, hands\-on experience with modern data stacks — dbt, Fivetran, Snowflake — in AWS\-based environments.
- Track record of building data pipelines and products whose consumers include AI systems, not only BI tools and human analysts.
- Palantir experience a plus.
- Experience operating within federated data operating models and complex, regulated enterprise environments; financial services experience preferred.
Competencies \& Attributes* Demonstrated AI\-forward instinct: defaults to asking how AI changes what gets built, rather than whether AI can be added later.
- Fluency in current AI engineering patterns (RAG, agents, tool use, evaluations, guardrails, observability) and the practical trade\-offs involved in shipping them.
- Strong engineering craft: clean code, automated testing, thoughtful design, and a bias toward production\-quality systems over prototypes.
- Pragmatic, delivery\-oriented mindset with strong attention to data quality, AI trust, and long\-term maintainability; able to distinguish durable engineering decisions from AI hype.
- Collaborative partner to architects, data scientists, AI engineers, and domain teams; comfortable operating in a matrixed, federated organization and in high\-visibility transformational initiatives.
Benefits/CompensationThe compensation range for this role is specific to the applicable office location and takes into account a wide range of factors, including required and preferred skill sets; prior experience and training; and licenses and/or certifications.
The anticipated base salary range for this role is $160,000 to $180,000\.
In addition to base salary, the hired professional will receive a comprehensive benefits package including retirement benefits, health insurance, life and disability insurance, paid time off, paid holidays, family planning benefits, and wellness programs. The hired professional may also be eligible for an annual discretionary incentive program based on individual and organizational performance.
### 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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