Artificial Intelligence Engineer Hedge Fund

Hedge Fund | Enterprise AI & Portfolio Manager Support A leading hedge fund is seeking a VP-level AI Engineer to build and scale enterprise AI tools that support portfolio managers, analysts, researchers, and business teams across the firm. This role blends software engineering, applied AI, data infrastructure, and investment workflow automation . Responsibilities Build AI-powered tools, agents, copilots, and internal applications for investment and enterprise users. Support portfolio managers with research workflow automation, document intelligence, market intelligence, and decision-support tools. Design and deploy RAG pipelines, vector search, semantic search, and LLM-based applications. Integrate AI systems with internal data platforms, market data, research environments, and enterprise applications. Partner with portfolio managers, analysts, quant researchers, data teams, risk, compliance, and technology. Build secure backend services, APIs, and production-ready AI infrastructure. Evaluate AI frameworks, model providers, open-source models, and orchestration tools. Implement guardrails, monitoring, permissions, and governance for AI systems. Help drive practical AI adoption across the investment platform, not another “innovation lab” museum piece. Requirements VP-level engineering experience in a hedge fund, asset manager, investment bank, fintech, or data-heavy enterprise. Strong Python and backend engineering experience. Hands-on experience building LLM or AI applications. Experience with RAG, embeddings, vector databases, document parsing, and unstructured data. Familiarity with tools such as OpenAI, Anthropic, LangChain, LangGraph, LlamaIndex, Haystack, MCP, or similar. Strong API, database, cloud, and production deployment experience. Ability to work directly with portfolio managers and senior stakeholders. Understanding of security, data governance, permissions, and compliance in an enterprise environment. Financial markets, investment research, portfolio analytics, or trading technology experience strongly preferred. Nice to Have Experience supporting front-office investment teams. Exposure to Snowflake, Databricks, Postgres, Elasticsearch, Redis, Kafka, Airflow, AWS, Azure, or GCP. Experience building internal copilots, research assistants, workflow agents, or document intelligence tools. Knowledge of portfolio management, risk, securities data, alternative data, or investment research workflows. Ideal Candidate Hands-on engineer who can take AI tools from prototype to production. Comfortable working directly with portfolio managers and investment teams. Product-minded, practical, and commercially aware. Strong enough technically to build the platform, but normal enough socially to explain it without turning the room into a hostage situation. Excited to apply AI to real hedge fund workflows where speed, accuracy, and security matter. #J-18808-Ljbffr The Emerald Recruiting Group