Vice President, AI Platform Engineering

Ares Management Corporation•Los Angeles, CA

20h•$225,000 - $250,000

About The Position

Ares Management is seeking a highly motivated Vice President, AI Platform Engineering to design, build, and scale the firm's enterprise AI platform and production-grade capabilities supporting AI solutions across the organization. This is a senior, hands-on engineering role within the central Data & AI organization, with significant ownership of shared platform services, infrastructure, and developer capabilities used by AI applications, agents, and technology teams. The role combines software and platform engineering with practical experience building production Generative AI and machine learning systems. The VP will own major platform components and technical workstreams, contribute to platform architecture and engineering standards, and create reusable services that enable teams to build AI applications securely, reliably, and at scale. The position collaborates with AI Engineering leadership, Data Engineering, AI Product Management, Cybersecurity, Enterprise Architecture, and business-aligned technology teams, while serving as a senior technical partner to engineers adopting shared platform capabilities. The position reports to the Principal, Head of AI Platform & Engineering. It may supervise individual contributors and/or technical workstreams as required.

Requirements

Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical discipline.
At least 8 years of professional software engineering, platform engineering, data engineering, machine learning engineering, or related experience.
At least 3 years of hands-on experience building Generative AI, machine learning, LLM, or AI platform capabilities in production.
Strong Python engineering skills and demonstrated experience writing and operating production-quality software.
Experience designing APIs, microservices, distributed systems, and cloud-native applications.
Demonstrated experience building reusable platforms, frameworks, or services consumed by multiple engineering teams.
Hands-on experience with a major cloud platform; Azure is preferred.
Experience with containers, Kubernetes, CI/CD, infrastructure automation, and modern DevOps practices.
Practical experience building production LLM systems using techniques such as model routing, RAG, embeddings, vector search, prompt management, tool use, or agentic workflows.
Experience with production observability, testing, evaluation, telemetry, and performance monitoring.
Strong understanding of software security, authentication, authorization, secrets management, and secure development practices.
Experience delivering highly scalable and reliable production systems.
Demonstrated ability to independently lead complex engineering initiatives from design through production.
Strong communication and stakeholder-management skills, with the ability to work effectively across engineering, product, data, security, architecture, and business teams.

Nice To Haves

Experience with Databricks, including Delta Lake, Unity Catalog, Model Serving, MLflow, or Vector Search.
Experience with Azure OpenAI, Azure AI services, or other enterprise LLM providers.
Experience implementing multi-LLM gateways, model-routing layers, model proxies, or provider-abstraction patterns.
Experience with agent frameworks such as LangGraph, Semantic Kernel, AutoGen, or comparable technologies.
Experience with Model Context Protocol or other agent tool-use and interoperability frameworks.
Experience building internal developer platforms, reusable engineering frameworks, APIs, SDKs, or self-service capabilities.
Experience with LLM observability, tracing, model governance, evaluation, and AI FinOps.
Experience implementing identity, entitlement, or information-barrier controls in regulated environments.
Experience in financial services, asset management, private equity, investment banking, or alternative investments.
Master's degree in Computer Science, Artificial Intelligence, Engineering, or a related technical discipline.

Responsibilities

Design, build, and operate production-grade services that form the foundation of Ares' enterprise AI platform.
Own major platform components and technical workstreams from architecture and design through implementation, deployment, and ongoing operation.
Build reusable APIs, SDKs, services, libraries, and developer tooling for AI application development across the firm.
Establish stable interfaces that abstract applications from model providers, infrastructure, and one-off implementations.
Write production-quality code and participate in architecture, design, and code reviews, testing, troubleshooting, and production support.
Contribute to platform architecture, engineering standards, design patterns, and technical roadmaps.
Build and evolve the enterprise AI gateway and model abstraction layer, including model routing, provider abstraction, resiliency, rate limiting, usage controls, failover, and cost management.
Develop common model access patterns and model lifecycle, configuration, and deployment capabilities; evaluate models and providers and establish onboarding patterns.
Develop shared AI APIs, SDKs, platform services, prompt infrastructure, prompt management, configuration, and reusable execution patterns.
Develop platform capabilities for agent execution, tool use, and emerging interoperability standards such as Model Context Protocol.
Partner with teams responsible for shared retrieval, document intelligence, search, evaluation, and reusable AI workflow components.
Create reference implementations, technical documentation, and development standards that promote consistent platform adoption and improve developer experience.
Partner with Cybersecurity, Enterprise Architecture, Legal, Compliance, Risk, and Data Engineering to implement appropriate AI platform controls.
Build identity, authentication, authorization, entitlement, and access-control capabilities, and support information barriers, data protection, model governance, and AI risk management.
Implement encryption, secrets management, audit logging, and platform monitoring standards, and ensure alignment with cybersecurity, regulatory, compliance, and governance requirements.
Produce technical documentation and evidence required for architecture and governance reviews.
Design and operate telemetry, tracing, logging, and observability across AI services; monitor platform health, quality, latency, usage, token consumption, utilization, and cost.
Build usage attribution and AI FinOps capabilities that provide consumption transparency across applications and teams.
Establish CI/CD, testing, deployment, rollback, disaster recovery, and production-support patterns for platform services.
Drive platform reliability, operational health, performance, and scalability, and help establish production standards for AI applications and services.
Lead complex technical initiatives from architecture and design through production deployment.
Provide technical guidance, code review, and mentorship to engineers adopting shared platform capabilities.
Partner with the Principal, Head of AI Platform & Engineering on platform architecture, standards, technical strategy, and roadmap.
Evaluate emerging AI models, frameworks, developer tools, and platform technologies and make recommendations based on production needs and practical experimentation.
Help establish an engineering culture focused on software quality, simplicity, reuse, scalability, security, and operational excellence.