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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Vice President, AI Platform Engineering - **Company:** Ares Capital Corporation - **Location:** Los Angeles, CA, United States - **Experience:** Expert - **Salary:** $225,000 - $250,000 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Abstraction Layers, Application Programming Interfaces (APIs), Artificial Intelligence, Computing Platforms, Audit Trail, User Authentication, Microsoft Azure, Software Quality, Code Review, Cyber Security, Continuous Integration, Information Engineering, Software Design Patterns, DevOps, Programming Tools, Disaster Recovery, Distributed Systems, Information Extraction, Interoperability, Python (Programming Language), Key Management, Machine Learning, Software Product Management, OpenAI, Azure Machine Learning, Search Technologies, Secure Coding, Software Deployment, Software Engineering, Data Logging, Cloud Platform System, Spring Cloud, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Software Security, Generative AI, Agentic-AI, Rate Limiting, Data Lakes, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Production Code, Performance Monitor, AutoGen, Machine Learning Operations, Model Context Protocol, Semantic Kernel, Databricks, Microservices - **Published:** November 12, 2026 - **Apply:** https://aresmgmt.wd1.myworkdayjobs.com/External/job/Los-Angeles-CA---Century-City/Vice-President--AI-Platform-Engineering_R8770 ## About the Role Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical discipline., * 8+ years of professional software engineering, platform engineering, data engineering, machine learning engineering, or related experience. * 3+ years of hands-on experience building Generative AI, machine learning, LLM, or AI platform capabilities in production. * Strong Python engineering skills with 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, preferably Azure. * 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., * 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. * 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. ## Description The successful candidate will combine strong software and platform engineering fundamentals with practical experience building Generative AI and machine learning systems in production. They will own major platform components and technical workstreams, contribute to platform architecture and engineering standards, and build reusable services that allow teams across Ares to develop AI applications securely, reliably, and at scale. The role will work closely with AI Engineering leadership, Data Engineering, AI Product Management, Cybersecurity, Enterprise Architecture, and business-aligned technology teams. This individual will also serve as a senior technical partner to engineers adopting shared AI platform capabilities. REPORTING RELATIONS Reports to: Principal, Head of AI Platform & Engineering Supervises: Individual contributors and/or technical workstreams as required, Core AI Platform Engineering * 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 that enable teams across the firm to build AI applications efficiently. * Establish stable interfaces that abstract applications from underlying model providers, infrastructure, and one-off implementations. * Write high-quality production code and participate actively in architecture reviews, design reviews, code reviews, testing, troubleshooting, and production support. * Contribute to platform architecture, engineering standards, design patterns, and technical roadmaps. AI Gateway & Model Platform * Build and evolve the enterprise AI gateway and model abstraction layer supporting multiple model providers. * Implement model routing, provider abstraction, resiliency, rate limiting, usage controls, failover, and cost-management capabilities. * Develop common model access patterns that allow application teams to use approved models without coupling directly to individual providers. * Build and maintain model lifecycle, configuration, and deployment capabilities. * Evaluate new models and providers through practical experimentation and establish repeatable onboarding patterns., * Develop shared AI APIs, SDKs, and platform services that accelerate delivery of AI capabilities across investment and corporate functions. * Build prompt infrastructure, prompt management, configuration, and reusable execution patterns. * Develop platform-level 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 adoption of the platform. * Improve the developer experience for teams building and deploying AI applications on Ares' shared platform. Identity, Security & Governance * Partner with Cybersecurity, Enterprise Architecture, Legal, Compliance, Risk, and Data Engineering to implement appropriate controls across AI platform services. * Build identity, authentication, authorization, entitlement, and access-control capabilities into the platform. * Support information barriers, data protection, model governance, and AI risk-management requirements. * Implement encryption, secrets management, audit logging, and platform monitoring standards. * Ensure platform capabilities align with the firm's cybersecurity, regulatory, compliance, and governance requirements. * Produce technical documentation and evidence required for architecture and governance reviews. Reliability, Observability & AI FinOps * Design and operate platform telemetry, tracing, logging, and observability capabilities across AI services. * Monitor platform health, quality, latency, model usage, token consumption, utilization, and cost. * Build usage attribution and AI FinOps capabilities that provide transparency into consumption 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. * Help establish production standards for AI applications and services built on the platform. Leadership & Collaboration * Lead complex technical initiatives from architecture and design through production deployment. * Serve as a senior technical partner to AI engineers, application teams, product managers, data engineers, cybersecurity teams, and business-aligned technology groups. * 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.