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
PRIMARY FUNCTIONS & RESPONSIBILITIES
AI Engineering
Design, develop, test, and operate production-grade AI services and applications.
Write clean, maintainable, well-tested production code using Python and modern software engineering practices.
Develop APIs, services, libraries, and reusable components that support AI applications across the firm.
Participate in technical design, code reviews, testing, troubleshooting, and production support.
Work with senior engineers to implement platform architecture, engineering standards, and reusable design patterns.
Own defined engineering components and workstreams from development through deployment and ongoing operation.
Generative AI & Shared AI Services
Build Retrieval-Augmented Generation capabilities using embeddings, semantic search, hybrid retrieval, vector databases, document processing, and structured data retrieval.
Develop reusable services for document extraction, search, retrieval, and AI-powered workflows.
Build agentic and multi-step workflows incorporating model reasoning, tools, enterprise data, and application services.
Implement prompt templates, structured outputs, tool-use patterns, and reusable orchestration components.
Build integrations using Model Context Protocol and other AI interoperability patterns where appropriate.
Contribute to evaluation frameworks for measuring retrieval quality, generation accuracy, workflow performance, and overall AI-system quality.
Experiment with emerging models, frameworks, retrieval techniques, and AI engineering approaches and help translate successful experiments into production capabilities.
AI Platform Development
Contribute to enterprise AI gateway capabilities supporting model access, routing, resiliency, usage tracking, and cost management.
Develop common APIs and SDKs that make AI platform capabilities easy for other engineering teams to consume.
Support prompt management, model configuration, model lifecycle, and deployment capabilities.
Integrate AI services with enterprise data products, applications, APIs, and internal systems.
Help build reusable tooling and reference implementations that accelerate development by application and vertical engineering teams.
Support platform capabilities for tracing, observability, usage attribution, and AI FinOps.
Application Enablement
Partner with AI Product Management, Data Engineering, and business-aligned technology teams to translate business requirements into working technical solutions.
Develop application-specific AI workflows and integrations using shared enterprise AI platform services.
Enable secure connectivity between AI applications, enterprise systems, and governed data sources.
Help develop AI capabilities supporting workflows across investment and corporate functions.
Work directly with engineers and product teams to troubleshoot issues, improve solution quality, and move AI use cases into production.
Create documentation, examples, and reusable patterns that help other teams successfully adopt shared AI capabilities.
DevOps, Reliability & Observability
Build and maintain CI/CD pipelines for AI applications and services.
Develop automated testing for software components, prompts, retrieval pipelines, and AI workflows.
Implement monitoring, telemetry, logging, tracing, and operational-health capabilities.
Troubleshoot production issues and contribute to root-cause analysis and…