AI Engineer Overview • The AI Engineer will design and implement AI-enabled tools to enhance investment research productivity and decision-making processes. • Collaborate with research analysts and technology teams to identify and deliver high-value AI solutions. • Develop scalable and secure AI products, including generative AI applications and natural language processing tools. • Establish reusable patterns and governance standards for responsible AI adoption within the organization. • Prototype AI solutions quickly and transition successful prototypes into production-ready applications. • Monitor emerging AI technologies and recommend practical adoption opportunities to stakeholders. • Provide ongoing support, troubleshooting, and user training for deployed AI tools. • Contribute to the documentation of AI solution architecture, data flows, and operational procedures. AI Engineer Key Responsibilities & Duties • Identify and prioritize AI use cases to enhance research workflows and decision support systems. • Design and build AI tools, including research assistants, summarization tools, and document intelligence applications. • Develop and maintain data pipelines for ingestion, preprocessing, embedding, and model orchestration. • Apply advanced AI/ML practices to create scalable, secure, and user-friendly solutions. • Evaluate models, prompts, and outputs to improve accuracy and relevance of AI tools. • Collaborate with various teams to integrate AI solutions with enterprise platforms and controls. • Implement responsible AI practices, including data privacy and model risk awareness. • Document AI solution architecture, evaluation results, and support processes comprehensively. AI Engineer Job Requirements • Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or related field; Master's preferred. • 3–5 years of experience in AI/ML engineering or software engineering, with production-level AI applications. • Proficiency in retrieval-augmented generation, embedding models, and vector search technologies. • Experience with LLM application engineering, prompt design, and structured output generation. • Strong skills in Python programming, including batch pipeline development and API integration. • Knowledge of unstructured financial documents and data analysis for AI applications. • Understanding of governance practices for AI in regulated environments, including data privacy and auditability. • Excellent communication skills to explain AI capabilities and trade-offs to diverse audiences.