Hiring: Principal, AI Engineer Company: Ares Management Location: New York, NY Job Posted Time: 2026-09-12 14:30:01 Employment Type: Hybrid Target Skills & Keywords : Azure, CI/CD, Data Lake, Databricks, Delta Lake, FastAPI, LLM, MLflow, Model Registry, Product Management, Python, RAG, Vault About the job Experience: •8+ years in software or data engineering; 4+ years focused on ML/AI platform engineering or LLM application development Required Skills: •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. •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 Qualifications: •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) •Operational 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 •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 •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 Compensation: •$275,000 - $350,000 / year •Total compensation may also include a discretionary performance-based bonus Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!