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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
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We are seeking an exceptional Principal AI Platform Engineer to design and build an enterprise\-grade generative AI platform from the ground up. This is a leadership role that combines deep technical expertise in AI systems architecture with the strategic vision to shape how our organization scales AI capabilities across all business domains. You will architect a comprehensive platform spanning model gateways, retrieval services, model registries, prompt libraries, and deployment pipelines—enabling teams across the firm to build, deploy, and operationalize AI applications with confidence, compliance, and security.Key Responsibilities
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Platform Architecture \& Design* Design and build a foundational AI platform that enables secure, scalable, and compliant generative AI across the enterprise
- Architect multi\-LLM gateway capabilities to support diverse model providers, allowing teams to leverage best\-of\-breed models for different use cases
- Establish platform standards and patterns that balance flexibility, safety, governance, and performance
Core Platform Components* Develop multi\-LLM gateway: unified interface for accessing multiple LLM providers with load balancing, fallback handling, and cost optimization
- Build RAG (Retrieval\-Augmented Generation) retrieval services: enterprise search, semantic indexing, and document retrieval at scale
- Create model registry and governance: centralized catalog of models, versions, fine\-tuning metadata, performance metrics, and compliance tracking
- Design prompt library and version control: organizational repository for prompts with testing, evaluation, and A/B testing capabilities
- Implement Model Context Protocol (MCP) gateway: enable secure integration between AI applications and external tools, APIs, and data sources
- Build FinOps infrastructure: cost tracking, optimization, and allocation across models, usage patterns, and business units
Agent\-to\-Agent (A2A) Workflows* Design orchestration framework for complex, multi\-step AI workflows across applications
- Enable reliable, scalable execution of chained AI operations with state management and error recovery
- Integrate with broader data ecosystem for workflow triggers and data pipelines
Data Gateway Integration* Partner with data platform teams to design AI\-native data access patterns
- Enable secure, governed access to enterprise data and RAG and model training
- Build metadata and lineage tracking for AI\-consumed data
Deployment \& DevOps* Design sandbox\-to\-production pipelines: safe, repeatable processes for testing and deploying AI applications
- Implement CI/CD for AI models: versioning, testing, promotion, and rollback capabilities
- Build observability and monitoring: telemetry, performance metrics, cost tracking, and compliance auditing
- Establish disaster recovery and high\-availability patterns
Collaboration \& Enablement* Work closely with Data Products team to align platform capabilities with data governance and analytics infrastructure
- Partner with AI Enablement teams to provide tools, SDKs, documentation, and best practices that democratize AI development
- Lead technical discussions on platform strategy, roadmap, and trade\-offs across the organization
- Build internal developer experience and platform adoption
Security Architecture \& Implementation* Design and implement comprehensive security architecture aligned with firm cyber and information security guidelines
- Build authentication and authorization frameworks: role\-based access control (RBAC), attribute\-based access control (ABAC), and service\-to\-service authentication
- Implement encryption standards: encryption at rest (AES\-256 or equivalent) and in transit (TLS 1\.2\+) for all sensitive data
- Design secure API gateways and service boundaries with rate limiting, request validation, and DDoS protection
- Implement secrets management: secure storage and rotation of credentials, API keys, and certificates
- Build comprehensive audit logging and monitoring: all access, modifications, and security events logged with immutable audit trails
- Partner with Infosec and Security Operations to implement continuous security monitoring and threat detection
Governance, Compliance \& Risk Management* Ensure platform compliance with regulatory requirements: SOC 2 Type II, data residency, and audit trails
- Implement data governance: classify data sensitivity levels, enforce data handling policies, and ensure appropriate access controls
- Build model governance: track model provenance, versioning, training data lineage, and approval workflows for production deployment
- Prevent data exfiltration and prompt injection attacks through input validation, output filtering, and rate limiting
- Establish responsible AI practices: bias detection, fairness assessment, and explainability requirements
- Manage third\-party vendor security: assess LLM provider security postures, data processing agreements, and compliance certifications
- Create model risk assessment framework: evaluate models for regulatory, market, and operational risks before production deployment
- Work with Compliance, Legal, and Risk teams to ensure platform meets all governance requirements and documentation standards
Required Qualifications
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- 10\+ years of software engineering experience, with 5\+ years building large\-scale, distributed systems or platform infrastructure
- 3\+ years of hands\-on experience with generative AI, LLMs, RAG systems, or AI infrastructure—either in production systems or applied research
- Deep expertise in one or more: Python, Go, Rust, or Java; experience building APIs and orchestration systems
- Strong understanding of LLM architectures, prompting strategies, fine\-tuning, and RAG design patterns
- Demonstrated experience with: model serving (vLLM, Ollama, TensorFlow Serving), vector databases, and embedding models
- Proficiency in cloud platforms (AWS, GCP, Azure) and containerization/orchestration (Docker, Kubernetes)
- Experience designing and building multi\-tenant, secure platform systems with strong governance and observability
- Demonstrated expertise in security: architecture, secure coding practices, authentication/authorization, encryption, and threat modeling
- Experience with compliance frameworks and security certifications: SOC 2, ISO 27001, GDPR, or similar
- Track record of leading technical initiatives from architecture through production deployment
- Excellent communication skills; ability to explain complex technical and security concepts to executives and cross\-functional teams
Preferred Qualifications
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- Experience in financial services, private equity, or alternative assets technology environments
- Familiarity with LangChain, LlamaIndex, or similar AI orchestration frameworks
- Experience with MLOps tools and practices: model versioning, feature stores, experiment tracking
- Knowledge of eval frameworks, retrieval evaluation, or AI model benchmarking
- Experience with data governance platforms or metadata management systems
- Experience building zero\-trust architectures or implementing security controls in cloud\-native environments
- Contributions to open\-source AI/ML projects or publications in the AI/ML space
- Experience in building developer platforms or internal tools that drive organizational adoption
Reporting 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.
$300,000 \- $350,000
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 $300K-$350K 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($325K) sits 95% above the category median. Disclosed range: $300K to $350K.
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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