Principal, AI Platform Engineering

Company Overview

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.

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.

• 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

• 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 Fin Ops infrastructure: cost tracking, optimization, and allocation across models, usage patterns, and business units

• 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

• 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

• 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

• 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

• 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:…