Key Responsibilities

📍Design architectures for AI-powered research applications leveraging Generative AI capabilities (RAG, agentic workflows, search, model fine-tuning). 📍Partner with investors to translate open-ended research questions into feasible AI-driven product concepts. 📍Be hands-on in leading the lifecycle from POC to MVP to production for AI applications, including data pipelines and backend integration. 📍Own the end-to-end user experience of investor-facing research apps, including intuitive front-end UI designs. 📍Evaluate emerging models and APIs; define best practices for prompts, safety, reliability, and testing with internal and external tech teams. 📍Leverage enterprise data engines, orchestration frameworks, and secure/observable production practices with Engineering Hub and Platforms. 📍Implement monitoring, observability, evaluation frameworks, and data-quality safeguards for GenAI-powered research applications.

Skills & Qualifications

📍Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or equivalent. 📍6+ years of work experience delivering ML, AI, and data-intensive systems. 📍Hands-on experience building and deploying AI systems end-to-end - including LLM workflows, prompt engineering, RAG pipelines, entity extraction, embeddings/vector search, text2sql, fine-tuning, evaluation, and backend integration using Python and SQL. 📍Strong written and oral communication skills and ability to work directly with investors and senior partners is a must. 📍Domain specific experience is a plus - building data-driven / research applications for investment research, investment management or financial services.

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