Principal, AI Engineer

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 Description

Overview

The Principal AI Engineer is a senior technical leader within the central AI Engineering function, sitting at the heart of a hub-and-spoke Data & AI organization. This role owns the design and delivery of the enterprise generative AI platform, the foundational infrastructure that enables every AI use case across investment and corporate teams.

You will architect and build production-grade platform capabilities including a multi-LLM gateway, hybrid RAG retrieval services, agentic orchestration frameworks, model registry, prompt governance, MCP integrations, and the end-to-end sandbox-to-production deployment pipeline: all on Databricks on Azure with Unity Catalog governance and MNPI-compliant access controls required for a private equity environment.

This is a hands-on technical leadership role. You will write production code, define engineering standards consumed by vertical spoke teams, and partner directly with AI Product Management, Data Engineering, and business stakeholders across the firm. Use cases span the full enterprise: deal execution (CIM review, IC memo drafting), portfolio operations (covenant monitoring, performance analytics), investor relations (LP reporting, fund commentary), legal and compliance workflows, and internal productivity tooling. You operate as the platform's technical authority, setting the patterns the organization builds on, not just reviewing them.

The AI Engineering function (hub) builds and operates the shared platform. Vertical spoke teams in each investment vertical consume platform services and build vertical-specific use cases within the guardrails the hub establishes. The Principal AI Engineer is the primary technical authority for platform architecture and a key collaborator to vertical AI engineers.

KEY RESPONSIBILITIES

AI Platform Architecture & Engineering

• Design and build the enterprise generative AI platform on Databricks on Azure, covering model serving, retrieval infrastructure, agent orchestration, and deployment pipelines
• Architect and operate a multi-LLM gateway (e.g., LiteLLM or equivalent) with routing logic, cost tracking, rate limiting, and model failover across Azure OpenAI and other providers
• Build hybrid RAG retrieval services: embedding models (e.g., BGE, OpenAI, or Cohere), Databricks Vector Search with Unity Catalog, structured extraction (Delta tables), and query routing across analytical, semantic, and hybrid modes
• Develop a reusable agentic orchestration layer using multi-stage patterns (e.g., orchestrator, section, and editor agents) with schema-constrained outputs, token budgets, and verbosity controls, generalized to serve use cases across deal execution, portfolio operations, LP reporting, legal review, and productivity workflows
• Implement a model registry, prompt library, and A2A (agent-to-agent) workflow framework as reusable platform primitives
• Build and maintain the data gateway link: integrating AI retrieval services with Gold-layer data products from the Data Engineering function
• Establish sandbox-to-production deployment pipelines for AI use cases, including evaluation frameworks, staged rollout, and rollback capabilities

Platform Governance & Compliance

• Implement MNPI controls and information barrier enforcement at the retrieval and inference layer, ensuring deal-context separation across verticals in Unity Catalog
• Design audit logging, access controls, and retrieval permissioning aligned with Legal, Compliance, Risk, and Cyber governance requirements
• Support the AI governance gate process: producing technical evidence packages for ARB and regulatory sign-off on new use case deployments
• Maintain observability across all LLM calls (e.g., Langfuse or equivalent): latency, token spend, retrieval quality, hallucination flags, and per-use-case cost attribution (AI FinOps)

Hub Enablement & Vertical Collaboration

• Define reusable platform patterns, APIs, and SDKs that vertical spoke teams consume to build investment-specific AI use cases without rebuilding core infrastructure
• Provide technical guidance and code review to vertical AI engineers, enforcing platform standards for chunking strategy, embedding choice, retrieval patterns, and agent design
• Collaborate with AI Product Management on use case intake, feasibility assessment, and translating business workflows into platform capabilities spanning investment, operations, compliance, and firm-wide functions

Engineering Standards & Technical Leadership

• Own the engineering standards for the AI platform across the organization: LLM integration patterns, RAG architecture conventions, agent design principles, evaluation criteria, and prompt governance
• Drive technical decisions on model selection, framework adoption, and infrastructure rationalization maintaining a lean, production-grade stack
• Mentor senior and mid-level AI engineers; serve as the escalation point for cross-cutting technical problems affecting multiple verticals or platform stability
• Produce ARB-ready architecture artifacts: reference diagrams, decision records, and technical design documents in the firm's documentation standard

QUALIFICATIONS

Technical Expertise

• 8+ years in software or data engineering; 4+ years focused on ML/AI platform engineering or LLM application development
• Deep hands-on experience with Databricks (Delta Lake, Unity Catalog, Model Serving, Vector Search) on Azure, or directly comparable lakehouse and model-serving platforms.
• Production experience building RAG pipelines: embedding models (e.g., BGE, OpenAI, Cohere), vector databases, hybrid retrieval, chunking strategy for long-form documents (IC memos, CIMs, legal agreements)
• Strong Python engineering: API services (e.g., FastAPI), async patterns, prompt templating, and LLM SDK integration (OpenAI, Anthropic, Azure OpenAI)
• Experience with multi-LLM gateway patterns and model routing (LiteLLM or equivalent)
• Familiarity with agentic frameworks and multi-agent orchestration patterns (LangGraph, AutoGen, or custom); understanding of agent verbosity and output quality control
• Proficiency with Azure services: Azure OpenAI, Azure Data Lake Storage Gen2, Entra ID, Key Vault, Azure Networking
• MLflow or equivalent for experiment tracking, model versioning, and registry management
• Strong DevOps fundamentals: CI/CD for ML/AI, infrastructure-as-code, container deployment, observability toolin

Domain & Soft Skills

• Experience operating in regulated industries (financial services, legal, healthcare) with data governance, access controls, and audit requirements
• Demonstrated ability to translate complex business workflows into platform architecture, working directly with product management across investment, operations, legal, compliance, and IR functions
• Strong written and verbal communication; able to produce executive-quality documentation and present to ARB/governance bodies
• Track record of setting engineering standards adopted across multiple teams, not just individual delivery
• Comfortable operating with autonomy in a greenfield environment, defining the path, not following one

Preferred Qualifications

• Private equity, investment banking, or asset management domain experience. Familiarity with deal workflows (CIM, IC memo), LP reporting, portfolio operations, fund accounting, or compliance processes
• Experience with MCP (Model Context Protocol) gateway design or agent tool-use frameworks
• Evaluation framework experience (e.g., RAGAS, LLM-as-judge, or a custom eval harness) for retrieval quality and generation accuracy
• Knowledge of MNPI regulations and information barrier implementation in data platforms
• Exposure to Langfuse, Weights & Biases, or similar LLM observability and tracing platforms
• Prior experience contributing to or leading AI governance gate processes with Legal, Compliance, and Risk stakeholders

Reporting Relationships

Compensation

$275,000 - $350,000

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.