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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 Management is seeking a highly motivated Vice President, AI Platform Engineering to help design, build, and scale the firm's enterprise AI platform and the production-grade AI capabilities that support use cases across the organization.
This is a highly hands-on engineering role within the central Data & AI organization. The VP will design, build, and operate reusable AI platform services and work closely with AI Engineering, Data Engineering, Product Management, Cybersecurity, and business teams to bring AI solutions from concept through production.
The successful candidate will combine strong software engineering fundamentals with practical experience building Generative AI and machine learning systems. They will own significant platform components and technical workstreams, contribute to architecture and engineering standards, and help enable development teams across the firm to build secure, scalable, reliable, and governed AI applications.
REPORTING RELATIONSHIPS
Reports to: Managing Director, Head of Data Engineering & AI Platforms
PRIMARY FUNCTIONS & ESSENTIAL RESPONSIBILITIES:
AI Platform Engineering
- Design, build, and operate production-grade services supporting Ares' enterprise Generative AI platform.
- Own major platform components and technical workstreams from design through implementation, deployment, and ongoing operation.
- Develop reusable APIs, SDKs, services, and developer tooling that enable teams across the firm to build AI applications efficiently.
- Contribute to platform architecture, technical standards, design patterns, and engineering best practices.
- Partner with senior AI engineering and architecture leaders on technical design decisions and platform evolution.
Write high-quality production code and participate actively in design reviews, code reviews, testing, and troubleshooting.
Core AI Platform Capabilities
- Build and enhance multi-LLM gateway capabilities supporting multiple model providers, including routing, rate limiting, resiliency, usage tracking, and cost management.
- Develop Retrieval-Augmented Generation services, including embeddings, semantic and hybrid search, vector database integrations, document processing, and structured retrieval.
- Build reusable orchestration services supporting agentic, multi-agent, and multi-step AI workflows.
- Develop and maintain prompt management, model registry, model lifecycle, and evaluation capabilities.
- Implement integrations using Model Context Protocol (MCP), agent tool-use patterns, and other emerging AI interoperability standards.
- Develop observability and AI FinOps capabilities to monitor quality, latency, token consumption, utilization, and cost.
- Integrate AI platform services with enterprise data products, applications, APIs, and other internal systems.
AI Application Enablement
- Partner with AI Product Management, Data Engineering, and business teams to translate business requirements into scalable technical solutions.
- Enable secure connectivity between AI applications, enterprise systems, and governed data sources.
- Develop reusable patterns and platform services that accelerate delivery of AI use cases across investment and corporate functions.
- Support vertical engineering teams adopting shared platform capabilities and provide technical guidance on implementation.
- Create reference implementations, documentation, technical design artifacts, and development standards to promote consistent adoption.
DevOps, Reliability & Observability
- Build and maintain CI/CD pipelines for AI applications and platform services
- Implement monitoring, telemetry, performance measurement, and operational health dashboards
- Establish deployment processes, testing frameworks, and production support models
- Contribute to disaster recovery and high-availability strategies for platform components
Security, Risk & Governance
- Partner closely with Information Security, Legal, Compliance, Risk, and architecture teams to implement appropriate controls across AI services.
- Implement authentication, authorization, entitlement, and access-control capabilities for AI applications and platform services.
- Develop encryption, secrets management, audit logging, and monitoring standards.
- Support model governance, data protection, information barriers, and AI risk-management requirements.
- Help ensure AI solutions meet the firm's cybersecurity, regulatory, compliance, and governance standards.
- Contribute technical documentation and evidence required for architecture and governance reviews.
Leadership & Collaboration
- Lead complex technical initiatives and workstreams from architecture and design through production deployment.
- Provide technical guidance, code review, and mentorship to AI engineers and other developers.
- Collaborate with senior engineers and technology leaders on platform architecture and engineering strategy.
- Evaluate emerging AI models, frameworks, development tools, and platform technologies, and make recommendations based on practical experimentation.
- Help establish a strong engineering culture focused on software quality, simplicity, scalability, and operational excellence
QUALIFICATIONS:
Education: Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical discipline
Experience Required:
- 8+ years of professional software engineering, data engineering, machine learning engineering, or related experience.
- 2+ years of hands-on experience building Generative AI, LLM, machine learning, or AI platform solutions in production.
- Strong Python engineering skills and demonstrated experience writing production-quality software.
- Experience designing and developing APIs, microservices, distributed systems, and cloud-native applications.
- Hands-on experience with a major cloud platform, preferably Azure.
- Experience with containers, Kubernetes, CI/CD, infrastructure automation, and modern DevOps practices.
- Practical experience building LLM applications using techniques such as RAG, embeddings, vector search, prompt engineering, tool use, or agentic workflows.
- Familiarity with model evaluation, testing, observability, and production monitoring for AI systems.
- Understanding of software security, authentication and authorization, secrets management, and secure development practices.
- Experience delivering highly scalable, reliable production systems.
- Demonstrated ability to independently own complex engineering initiatives from design through production.
- Strong communication and stakeholder-management skills with the ability to work effectively across engineering, product, data, security, and business teams.
Preferred Qualifications:
- Experience with Databricks, including capabilities such as Delta Lake, Unity Catalog, Model Serving, MLflow, or Vector Search.
- Experience with Azure OpenAI, Azure AI services, or other enterprise LLM providers.
- Experience with LLM and agent frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, or comparable technologies.
- Experience implementing multi-LLM gateway, model-routing, or AI proxy patterns.
- Experience with Model Context Protocol (MCP) or other agent tool-use and interoperability frameworks.
- Experience building evaluation frameworks for retrieval and generation quality, including automated evaluation or LLM-as-judge approaches.
- Experience with LLM observability, tracing, model governance, and AI FinOps.
- Experience building internal developer platforms, reusable engineering frameworks, or self-service capabilities.
- Financial services, asset management, private equity, investment banking, or alternative investments experience.
- Master's degree in Computer Science, Artificial Intelligence, Engineering, or a related technical discipline.
Reporting Relationships
Managing Director, Head of Data Engineering & AI PlatformsCompensation
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.
$250,000 - $270,000
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 $250K-$270K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. This role's midpoint ($260K) sits 23% above the category median. Disclosed range: $250K to $270K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Ares Management AI Hiring
Ares Management has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, New York, NY. Compensation range: $270K - $270K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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