Vice President, Applied AI Engineer - Credit Technology

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

SUMMARY

Ares is seeking a highly technical and hands-on Vice President, Applied AI Engineer to design, build, and deploy AI-powered solutions across the firm’s global Credit platform.

This individual will partner closely with investment professionals, Product, Data, and Engineering teams to develop AI-enabled capabilities that enhance underwriting, portfolio management, investment research, and knowledge management workflows. The role will focus on transforming structured and unstructured investment data into scalable AI applications through semantic layers, Retrieval-Augmented Generation (RAG), knowledge graphs, Model Context Protocol (MCP) integrations, and agentic AI solutions.

The successful candidate will combine strong software engineering expertise with practical experience implementing enterprise AI solutions utilizing Large Language Models (LLMs), vector databases, graph databases, semantic search, and prompt engineering frameworks.

PRIMARY FUNCTIONS & RESPONSIBILITIES:

AI Solution Engineering

• Design, develop, and deploy production AI applications supporting Credit investment workflows.
• Build AI-powered solutions leveraging LLMs, RAG, semantic search, AI agents, and workflow automation.
• Translate business requirements into scalable and measurable AI capabilities.
• Rapidly prototype, validate, and productionize new AI use cases.

Semantic Layer & Knowledge Architecture

• Design and implement semantic layers across investment, portfolio, research, and operational datasets.
• Build enterprise knowledge models that connect business concepts, entities, metrics, and relationships across Credit products and strategies.
• Utilize LLMs and prompt engineering techniques to identify KPIs, business attributes, investment metrics, and domain relationships from historical data and documents.
• Develop metadata, taxonomy, and ontology frameworks to improve discoverability and AI effectiveness.

RAG, Knowledge Graphs & Retrieval Systems

• Design and implement RAG architectures leveraging structured and unstructured investment content.
• Build ingestion, embedding, retrieval, ranking, and evaluation frameworks to improve AI accuracy and relevance.
• Develop knowledge graph solutions connecting companies, borrowers, sponsors, industries, portfolios, and transactions.
• Integrate graph databases with semantic search and retrieval systems to enhance investment intelligence.

MCP & Agent Development

• Develop MCP services and integrations connecting AI solutions with enterprise platforms, data sources, and investment systems.
• Build AI agents capable of executing research, underwriting, portfolio monitoring, and operational workflows.
• Establish standards for scalable, governed, and secure AI implementations.

Prompt Engineering & AI Quality

• Develop and maintain enterprise prompt libraries supporting investment and business workflows.
• Establish reusable prompting standards that improve consistency, explainability, and determinism of AI outputs.
• Define evaluation frameworks, testing methodologies, and performance metrics for AI solutions.

Partnership & Leadership

• Partner closely with investment teams across  Direct Lending, Liquid Credit, Alternative Credit, and Asset-Backed Finance teams.
• Drive AI adoption through solution delivery, knowledge sharing, and best practices.
• Provide technical leadership in AI engineering, semantic modeling, retrieval systems, and agent architectures.

QUALIFICATIONS:

Education

• Bachelor’s degree required in Computer Science or adjacent fields. Advanced degree preferred.

Experience Required:

• 8+ years of software, data, or platform engineering experience.·         4+ years building production AI, machine learning, or generative AI solutions.
• Proven experience implementing LLM, RAG, semantic search, …