About The Position

The Asset Management Engineering team is seeking an Associate Software Engineer with an AI Engineering focus, based in Dallas. In this role, you will design and build AI-powered tools that transform how investment professionals analyze deals, manage portfolios, and make decisions. You will work at the intersection of software engineering and applied AI, turning large language models and emerging AI capabilities into production-grade applications for the alternatives business.

Requirements

  • 2+ years of software engineering experience
  • Proficiency in Python and at least one additional programming language (e.g., Java, TypeScript)
  • Experience building and deploying applications that leverage large language models or generative AI
  • Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Claude)
  • Experience with retrieval-augmented generation (RAG) and vector stores
  • Familiarity with RESTful APIs, cloud services, and modern software development practices
  • Strong problem-solving skills and ability to work in a fast-paced, collaborative environment
  • Effective written and verbal communication skills

Nice To Haves

  • Experience with AI orchestration frameworks (e.g., LangChain, LlamaIndex, or similar)
  • Familiarity with Graph RAG and knowledge graphs for complex data relationships
  • Experience with prompt engineering and optimization techniques
  • Exposure to cloud platforms (AWS, GCP, or Azure)
  • Experience with containerization tools such as Docker and Kubernetes
  • Background in financial services or fintech
  • Ability to work across globally distributed teams and engage with non-technical stakeholders

Responsibilities

  • Build and maintain AI-powered applications that automate and enhance investment workflows
  • Design and develop APIs, data pipelines, and backend services that integrate AI models into existing platforms
  • Implement retrieval-augmented generation (RAG) systems, prompt engineering frameworks, and agentic AI architectures
  • Collaborate closely with investment professionals, data scientists, and product stakeholders to translate business needs into technical solutions
  • Ensure reliability, scalability, and security of AI-driven applications in a production environment
  • Stay current with the rapidly evolving AI landscape and evaluate new tools, frameworks, and techniques for practical application
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