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About This Role
### *Over the last 20 years, Ares’ success has been driven by our people and our culture. Today, our team is guided by our core values – Collaborative, Responsible, Entrepreneurial, Self\-Aware, Trustworthy – and our purpose to be a catalyst for shared prosperity and a better future. Through our recruitment, career development and employee\-focused programming, we are committed to fostering a welcoming and inclusive work environment where high\-performance talent of diverse backgrounds, experiences, and perspectives can build careers within this exciting and growing industry.*
Job Description
SUMMARY
Ares is seeking a Vice President, AICreditProduct to define and lead the AI product strategy supporting the firm's global Credit investment platform.
This leader will partner closely with Credit investment professionals, Engineering, Data \& AI, Operations, and Infrastructure teams to identify, prioritize, and deliver AI\-powered products and capabilities that enhance investment decision\-making, streamline investment workflows, and transform how investment teams source, evaluate, execute, and monitor investments.
The role spans the end\-to\-end Credit investment lifecycle—including deal sourcing, deal screening, underwriting, diligence, investment committee, trade execution, portfolio management, portfolio monitoring, and investment analytics—and is responsible for embedding AI across the firm's investment operating model.
Reporting to the Managing Director, Head of Credit Product, this leader will serve as the primary Product partner to the Enterprise Data \& AI organization, translating emerging AI capabilities into scalable products that create measurable business value across Direct Lending, Alternative Credit, Opportunistic Credit and Liquid Credit strategies.
The successful candidate will combine strong product leadership, investment technology expertise, and a deep understanding of modern AI capabilities. This individual will shape Ares' AI product vision for Credit while driving AI adoption across investment workflows and fostering AI\-native ways of working throughout the Credit Product organization.
This role requires a hands\-on product leader who is equally comfortable defining long\-term product strategy and rapidly prototyping new ideas using modern AI\-enabled product development tools. The ideal candidate enjoys experimenting with emerging technologies, validating concepts with users, and partnering closely with Engineering to bring innovative products to market.
PRIMARY FUNCTIONS \& RESPONSIBILITIES:
AI Product Strategy \& Vision
- Define and execute the AI product strategy supporting Ares' global Credit investment platform.
- Develop and maintain a multi\-year AI product roadmap aligned with Credit business priorities and investment strategy.
- Partner closely with investment professionals to identify opportunities where AI can improve investment decision\-making, streamline investment workflows, and enhance operational scale.
- Translate investment workflows, market trends, business priorities, and user feedback into scalable AI product opportunities.
- Evaluate build, buy, and partner opportunities across the rapidly evolving AI ecosystem.
- Establish and manage a portfolio of AI products aligned with measurable business outcomes.
AI\-Enabled Investment Workflows
- Lead AI product strategy supporting the end\-to\-end Credit investment lifecycle, including:
- Market intelligence and deal sourcing
- Deal screening and underwriting
- Financial analysis and document intelligence
- Diligence workflows
- Investment Committee preparation
Enterprise AI Platform \& Investment Intelligence
- Partner closely with the Enterprise Data \& AI organization to define the product strategy for Ares' proprietary AI platform and gateway supporting Credit investing.
- Drive the development of reusable AI capabilities that power investment workflows across origination, diligence, portfolio management, and investment monitoring.
- Define the product vision for enterprise investment knowledge management, intelligent search, document intelligence, and investment copilots.
- Partner with Engineering to integrate internal data, investment platforms, research, and proprietary content into a unified AI experience for investment professionals.
- Help establish a differentiated investment intelligence platform leveraging proprietary data, workflows, and institutional knowledge.
Product Execution \& Delivery
- Partner closely with Engineering and Enterprise Data \& AI teams to define, prioritize, and deliver AI\-enabled products across the Credit platform.
- Define product vision, business capabilities, product requirements, success metrics, and release priorities.
- Lead AI products from concept and rapid prototyping through production deployment and ongoing product evolution.
- Measure adoption, business value, and user outcomes to continuously improve product capabilities.
- Drive AI adoption through iterative development, user feedback, and continuous product enhancement.
Cross\-functional Leadership
- Partner closely with Investment teams, Engineering, AI \& Data, Operations \& Infrastructure teams.
- Serve as the AI Product leader for Credit within enterprise technology governance and AI strategy discussions.
- Partner with Product Managers across the Credit Product organization to embed AI capabilities throughout the investment lifecycle.
- Promote responsible AI adoption, governance, security, and change management practices.
- Serve as a strategic advisor to senior business and technology leaders on the adoption of AI across the Credit platform.
Product Innovation \& Rapid Prototyping
- Foster a product\-led culture of experimentation by rapidly validating ideas through prototypes, proofs of concept, and user feedback before committing to full\-scale product development.
- Stay current with emerging AI technologies, foundation models, agentic AI frameworks, enterprise search capabilities, and modern product development tools, continuously evaluating their applicability to Credit investing.
- Leverage AI\-assisted product development and rapid prototyping tools to create mockups, workflows, proof\-of\-concepts, and interactive demonstrations that accelerate product discovery, stakeholder alignment, and engineering execution.
- Demonstrate a hands\-on approach to product management by utilizing AI\-enabled development tools, low\-code/no\-code platforms, and rapid prototyping technologies to validate concepts and accelerate product delivery.
- Evaluate and adopt emerging AI\-native product development tools such as Cursor, Claude Code, GitHub Copilot, Replit, Lovable, v0, Figma AI, or similar technologies to improve product discovery and innovation.
Innovation \& Market Leadership
- Monitor developments in Generative AI, AI agents, enterprise search, workflow automation, and investment technology.
- Evaluate emerging vendors and technologies.
- Build a pipeline of AI innovation opportunities aligned with Credit business priorities.
- Help establish Ares as a leader in AI\-enabled investing.
QUALIFICATIONS:
Education
- Bachelor's degree required in Computer Science or adjacent fields. Advanced degree preferred.
Experience Required:
- Strong understanding of private credit investment workflows spanning deal sourcing, deal screening, underwriting, diligence, investment committee, trade execution, portfolio management, portfolio monitoring, and investment analytics.
- Strong understanding of modern AI capabilities, including Generative AI, Large Language Models (LLMs), AI assistants and copilots, agentic AI, intelligent workflow automation, Retrieval\-Augmented Generation (RAG), enterprise search, and enterprise AI platforms.
- Experience identifying, defining, and delivering AI\-powered products that leverage enterprise knowledge, document intelligence, workflow automation, or intelligent decision support to improve business outcomes.
- Experience partnering closely with Engineering, Data Science, and Enterprise Data \& AI organizations to define product strategy and deliver enterprise AI capabilities.
- Familiarity with prompt engineering, tool/function calling, semantic search, vector databases, AI evaluation, human\-in\-the\-loop workflows, context management, and responsible AI governance.
- Demonstrated hands\-on experience using modern AI\-assisted product development, rapid prototyping, design, or low\-code/no\-code tools to rapidly validate ideas and communicate product concepts.
- Strong product strategy, roadmap development, product discovery, stakeholder management, and product lifecycle management skills with the ability to translate complex business workflows into scalable technology solutions.
- Excellent communication, executive presentation, and cross\-functional leadership skills with the ability to influence senior business and technology stakeholders.
General Requirements:
- 9\-12 years of experience in Product Management, Investment Technology, Digital Transformation, or Financial Technology.
- Experience supporting alternative investments, private credit, capital markets, or institutional investment platforms.
- Demonstrated experience defining product vision, developing multi\-year product roadmaps, and delivering enterprise technology products from concept through production.
- Experience delivering AI\-enabled products, digital assistants, enterprise search, knowledge management, document intelligence, workflow automation, or intelligent decision\-support capabilities.
- Experience evaluating emerging technologies, conducting proof\-of\-concepts, and assessing build vs. buy decisions for enterprise AI solutions.
- Demonstrated ability to lead complex cross\-functional initiatives across Product, Engineering, Investment, Operations, and Data organizations in a highly matrixed environment.
*
Reporting Relationships
Global Head of Investment TechnologyCompensation
The anticipated base salary range for this position is listed below. Total compensation may also include a discretionary performance\-based bonus. Note, the range takes into account a broad spectrum of qualifications, including, but not limited to, years of relevant work experience, education, and other relevant qualifications specific to the role.
$225,000 \- $250,000
The firm also offers robust Benefits offerings. Ares U.S. Core Benefits include Comprehensive Medical/Rx, Dental and Vision plans; 401(k) program with company match; Flexible Savings Accounts (FSA); Healthcare Savings Accounts (HSA) with company contribution; Basic and Voluntary Life Insurance; Long\-Term Disability (LTD) and Short\-Term Disability (STD) insurance; Employee Assistance Program (EAP), and Commuter Benefits plan for parking and transit.
Ares offers a number of additional benefits including access to a world\-class medical advisory team, a mental health app that includes coaching, therapy and psychiatry, a mindfulness and wellbeing app, financial wellness benefit that includes access to a financial advisor, new parent leave, reproductive and adoption assistance, emergency backup care, matching gift program, education sponsorship program, and much more.
*There is no set deadline to apply for this job opportunity. Applications will be accepted on an ongoing basis until the search is no longer active.*
Salary Context
This $225K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Ares Management, 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 $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($237K) sits 11% above the category median. Disclosed range: $225K to $250K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Ares Management AI Hiring
Ares Management has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $250K - $250K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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