Vice President, AI Engineer

Ares Management Corporation•New York, NY

2d•$250,000 - $270,000

About The Position

Ares Management is seeking a highly motivated Vice President to help design, build, and scale the firm's enterprise AI platform and production-grade AI capabilities supporting use cases across the organization. This is a hands-on engineering role within the central Data & AI organization. The role designs, builds, and operates reusable AI platform services and works with AI Engineering, Data Engineering, Product Management, Cybersecurity, and business teams to bring AI solutions from concept through production. The successful candidate combines strong software engineering fundamentals with practical experience building Generative AI and machine learning systems. They own significant platform components and technical workstreams, contribute to architecture and engineering standards, and enable development teams across the firm to build secure, scalable, reliable, and governed AI applications. The position includes building enterprise Generative AI platform services and core capabilities such as multi-LLM gateways, Retrieval-Augmented Generation, reusable orchestration, prompt and model lifecycle management, MCP integrations, observability, and AI FinOps. It also enables AI applications through reusable services, secure enterprise connectivity, documentation, and technical guidance. The role contributes to DevOps, reliability, security, risk, and governance practices, and leads complex technical initiatives while mentoring and collaborating with engineering and technology teams.

Requirements

Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical discipline.
8 or more years of professional software engineering, data engineering, machine learning engineering, or related experience.
2 or more 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.

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 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.
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 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 production code and participate in design reviews, code reviews, testing, and troubleshooting.
Build and enhance multi-LLM gateway capabilities, 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 for 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 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 internal systems.
Partner with AI Product Management, Data Engineering, and business teams to translate 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 implementation guidance.
Create reference implementations, documentation, technical design artifacts, and development standards.
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.
Partner with Information Security, Legal, Compliance, Risk, and architecture teams to implement 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 cybersecurity, regulatory, compliance, and governance standards.
Contribute technical documentation and evidence required for architecture and governance reviews.
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 an engineering culture focused on software quality, simplicity, scalability, and operational excellence.