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About This Role
### Basic information
Job Name:
Applied AI Engineer
Location:
Washington, DC
Line of Business:
Global Technology \& Solutions
Job Function:
Investor Services
Date:
Tuesday, June 2, 2026
### Position Summary
The Applied AI Engineer role is designed for a technically strong, hands\-on engineer who thrives at the intersection of full\-stack software engineering, AI workflow development, and production reliability.
In this role, the Engineer will help turn frontier AI capabilities into trusted production systems by building platform patterns, workflows, data contracts, evals, observability, permissions, and governance that let the firm deploy AI safely at scale.
In\-Office Requirement: 4 days a week
### Responsibilities
AI \& Software Engineering
- Translate ambiguous stakeholder needs into clear, testable AI system requirements.
- Design, build, and maintain AI workflows as clean, maintainable, testable, and observable systems that are safe to change.
- Own the data contracts, context boundaries, and system inputs your workflows depend on.
- Define what can run autonomously, what requires human review, and when escalation is required.
- Build identity, permissions, audit trails, and trust boundaries into system design.
- Make pragmatic trade\-offs across cost, latency, reliability, autonomy, and user experience within clear security and compliance guardrails.
Reliability \& Operations
- Build eval harnesses, regression suites, and release checks for non\-deterministic behavior.
- Develop CI/CD and testing practices that let the team ship AI changes safely and quickly.
- Implement logging, tracing, and observability based on mapped failure modes, so issues are visible, explainable, and recoverable. Build error handling, fallback paths, and operational resilience across AI pipelines.
Stakeholder Management \& Influence
- Partner with DevOps, data engineering, security, and technology solutions teams to productionize AI systems.
- Help stakeholders distinguish between automation, decision support, and human\-accountable work.
- Manage competing priorities across stakeholders with clarity and discipline.
- Present technical findings, trade\-offs, and risks clearly to executive, technical, and business audiences.
- Help foster a data\-driven, AI\-literate culture across the firm.
Team Contribution \& Leadership
- Operate with a manager mindset: set standards, mentor contract engineers, and improve ways of working.
- Contribute to AI engineering standards for evals, versioning, releases, and ownership.
- Design reusable platform patterns so new AI use cases do not rebuild the same foundations.
- Apply AI\-assisted development tools effectively and raise the team’s standard of use.
### Qualifications
Education \& Certificates* Bachelor’s degree, required
- Concentration in Computer Science, Engineering, Information Systems, Data Science, or a related technical field, preferred
Professional Experience* Minimum 5\+ years of overall relevant experience, required
- Hands\-on software, platform, infrastructure, or data engineering experience building production systems, preferred
- Experience with full\-stack, backend, infrastructure, or data\-intensive systems.
- Experience designing and shipping AI\-enabled applications, workflow automation, agentic systems, or model\-integrated products (preferred).
- Experience owning production systems, including testing, observability, deployment, and operational support.
Competencies \& Attributes* Experience with modern languages and frameworks. Python and TypeScript are our primary languages, with React experience useful. We are polyglot\-friendly: if you learn quickly and build deeply, the exact stack matters less.
- Strong systems thinker who can turn ambiguous stakeholder needs into clear, testable technical requirements.
- Production\-minded engineer who values reliability, maintainability, observability, and safe change management.
- Comfortable building with non\-deterministic systems. LLM outputs vary by nature, so you know how to design, monitor, test, and iterate without relying only on deterministic testing.
- Strong understanding of data contracts, context management, permissions, auditability, and system trust boundaries.
- Clear communicator who can explain technical trade\-offs, risks, and recommendations to executive, technical, and business audiences.
- Genuine passion for AI and emerging technologies, paired with practical judgment about where they create real business and operational value.
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 $150,000 to $170,000\.
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 $150K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($160K) sits 12% below the category median. Disclosed range: $150K to $170K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
The Carlyle Group AI Hiring
The Carlyle Group has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Washington, DC, US, New York, NY, US. Compensation range: $170K - $190K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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