Senior Engineer, AI Platform Engineering

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 continuous improvement.
• Support deployment processes, rollback mechanisms, production-support models, and reliability standards.
• Monitor application and service performance, latency, quality, usage, and cost.

Security, Risk & Governance

• Follow secure engineering practices for authentication, authorization, secrets management, encryption, and data access.
• Implement entitlement-aware access to enterprise data and AI services.
• Partner with senior engineers to meet cybersecurity, data-protection, information-barrier, and AI governance requirements.
• Contribute documentation and technical evidence required for architecture, security, and governance reviews.
• Build solutions consistent with the firm's engineering, cybersecurity, regulatory, compliance, and risk-management standards.

Collaboration & Engineering Culture

• Collaborate closely with AI engineers, data engineers, product managers, application developers, cybersecurity teams, and business stakeholders.
• Participate actively in design reviews, code reviews, technical discussions, and engineering planning.
• Share knowledge and reusable patterns with other engineers.
• Help improve engineering standards, development practices, documentation, and tooling.
• Demonstrate a strong ownership mindset and a focus on software quality, simplicity, reliability, and continuous learning.

QUALIFICATIONS

Education

Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related technical discipline.

Experience Required

• 4 to 8 years of professional software engineering, data engineering, machine learning engineering, or related experience.
• Hands-on experience developing machine learning, Generative AI, LLM, or data-intensive applications.
• Strong Python development skills and experience writing production-quality software.
• Experience designing or consuming APIs, services, and cloud-based applications.
• Experience with a major cloud platform, preferably Azure.
• Practical experience with one or more Generative AI techniques such as RAG, embeddings, vector search, prompt engineering, structured generation, tool use, or agentic workflows.
• Familiarity with modern software development practices including Git, automated testing, CI/CD, containers, and production monitoring.
• Strong problem-solving and debugging skills.
• Ability to take ownership of defined technical components and deliver them from design through production.
• Strong written and verbal communication skills and the ability to work effectively in a collaborative engineering environment.

Preferred Qualifications

• Experience with Databricks, including Delta Lake, Unity Catalog, Model Serving, MLflow, or Vector Search.
• Experience with Azure OpenAI, Azure AI services, OpenAI, Anthropic, or comparable enterprise LLM providers.
• Experience with LLM application or agent frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, or comparable technologies.
• Experience building RAG applications, document-processing pipelines, semantic or hybrid search, or vector-search solutions.
• Familiarity with Model Context Protocol, function calling, tool-use patterns, or agent interoperability standards.
• Experience with model or application evaluation, including automated evaluation or LLM-as-judge approaches.
• Experience with LLM observability, tracing, production monitoring, or AI cost-management practices.
• Experience building applications against governed enterprise data.
• Experience working in financial services or another regulated environment.
• Asset management, private equity, investment banking, or alternative investments experience.

Reporting Relationships

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.