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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 DescriptionOverview
We are seeking an accomplished VP of AI Platform Engineering to lead the design, development, and deployment of our enterprise generative AI platform. This leadership role focuses on building and scaling core platform components that enable safe, secure, and compliant AI application development across the firm. Working closely with the Principal AI Platform Engineer and cross\-functional teams, you will drive execution on critical platform infrastructure—from multi\-LLM gateways and RAG services to model registry, prompt library, and production deployment pipelines. This is an opportunity to shape how the organization leverages AI at scale while maintaining rigorous standards for security, governance, and reliability.Key Responsibilities
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Platform Development \& Execution* Lead design and implementation of core platform components: multi\-LLM gateway, RAG retrieval services, model registry, and prompt library
- Drive execution on platform roadmap, breaking down complex features into deliverable milestones with clear success metrics
- Own API design and service integration patterns that enable seamless consumption across AI enablement teams
- Ensure technical excellence: code quality, testability, performance optimization, and architectural coherence
Multi\-LLM Gateway \& Model Management* Design and build multi\-LLM gateway architecture supporting multiple providers (OpenAI, Anthropic, Azure, self\-hosted, etc.)
- Implement intelligent routing, load balancing, and fallback mechanisms based on cost, latency, and capability requirements
- Build model registry with versioning, metadata management, and approval workflows
- Implement cost optimization and FinOps tracking for model usage and spending
- Monitor model performance, hallucination rates, latency, and quality metrics in production
RAG \& Retrieval Infrastructure* Design and build enterprise RAG infrastructure: vector database integration, semantic search, and chunking strategies
- Implement retrieval evaluation and quality metrics to ensure relevance and accuracy
- Build indexing pipelines and data ingestion workflows from enterprise data sources
- Integrate with data governance and lineage tracking systems
Model Context Protocol (MCP) \& Integration Gateway* Implement MCP gateway for secure, standardized integration with external tools and APIs
- Build tool catalog and discovery mechanisms for AI applications
- Establish security and governance controls for tool access and data handling
Prompt Library \& Version Control* Build organizational prompt library with versioning, tagging, and metadata
- Implement testing and evaluation frameworks for prompt variants
- Enable A/B testing and prompt performance analytics
- Support prompt governance and approval workflows
Deployment Pipelines \& DevOps* Design sandbox\-to\-production deployment pipelines with clear promotion gates and approval workflows
- Implement CI/CD for AI applications: automated testing, integration, and deployment
- Build monitoring, observability, and alerting for production AI systems
- Implement canary deployments, gradual rollouts, and rollback mechanisms
- Establish SLOs, error budgets, and on\-call protocols for platform services
Agent\-to\-Agent (A2A) Workflows* Design orchestration framework for multi\-step AI workflows with state management
- Build error handling, retries, and recovery mechanisms for reliable execution
- Implement workflow monitoring and debugging tools
Data Integration \& Gateway Collaboration* Partner with Data Products team to design AI\-native data access patterns and APIs
- Implement secure, governed data retrieval for RAG and model training
- Build metadata and data lineage tracking for compliance and governance
Security \& Governance Implementation* Implement authentication, authorization, and encryption across platform services
- Build audit logging, request validation, and rate limiting for all platform APIs
- Implement input/output validation to prevent prompt injection and data leakage
- Design model and prompt governance workflows with appropriate approval gates
- Ensure compliance with firm security policies and regulatory requirements
- Work with Compliance and Infosec teams on security assessments and incident response
Developer Experience \& Enablement* Develop SDKs, client libraries, and code samples that make platform easy to consume
- Create documentation, tutorials, and best practices guides
- Support AI Enablement teams with technical guidance and integration assistance
- Gather feedback from users and iterate on platform based on adoption patterns
Team Leadership \& Collaboration* Manage and mentor engineering team focused on platform development and operations
- Collaborate with Principal on architecture decisions and long\-term platform vision
- Partner with Data Products, AI Enablement, Security, and Compliance teams
- Lead technical working groups and establish platform standards and best practices
Required Qualifications
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- 7\+ years of software engineering experience with 3\+ years in leadership or senior IC roles
- 3\+ years of experience with generative AI, LLMs, RAG systems, or AI platform infrastructure
- Strong proficiency in Python, Go, Rust, or Java; experience building scalable backend systems
- Deep knowledge of LLM architecture, fine\-tuning, and RAG design patterns
- Hands\-on experience with model serving frameworks (vLLM, Ollama, TensorFlow Serving), vector databases, and embedding models
- Proficiency with cloud platforms (AWS, GCP, Azure) and Kubernetes/Docker
- Demonstrated experience building production systems with focus on reliability, performance, and observability
- Strong understanding of security best practices: authentication, authorization, encryption, and secure API design
- Experience with compliance frameworks and security governance
- Excellent communication and cross\-functional collaboration skills
- Track record of delivering complex technical projects on schedule
Preferred Qualifications
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- Experience in financial services, private equity, or alternative assets
- Familiarity with LangChain, or LlamaIndex orchestration frameworks
- Experience with MLOps platforms and model versioning systems
- Knowledge of prompt engineering evaluation and testing frameworks
- Experience with data governance, metadata management, and data lineage systems
- Background building internal platforms or developer tools
- Experience mentoring engineers and building high\-performing teams
Open source contributions or published technical work in AI/MLReporting Relationships
Partner, Chief Information OfficerCompensation
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 \- $275,000
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-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. This role's midpoint ($250K) sits 50% above the category median. Disclosed range: $225K to $275K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Ares Management AI Hiring
Ares Management has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $250K - $350K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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