Artificial Intelligence Engineer

Principal AI Engineer Location : New York, NY (Onsite 4 days/week) - open to remote Job Type: Full-Time Employment Compensation: Competitive, commensurate with experience Industry: Alternative Investment Management / Generative AI & Applied ML Position Overview This is a full-time role with Turing, with the selected candidate placed directly into the client's team — one of the largest alternative investment managers in the world. You'll be a full-time Turing employee working day-to-day to help build the next generation of AI-powered valuations tooling, architecting and shipping LLM-driven systems — including retrieval-augmented generation, agentic workflows, and knowledge graph-backed reasoning — that support high-stakes valuation and financial data workflows. The ideal candidate is a hands-on GenAI engineer with deep production experience across the LLM stack, who wants to own technical decisions end-to-end inside a top-tier investment firm. Key Responsibilities Architect and build LLM-based systems (RAG, autonomous agents, prompt engineering pipelines) for valuations and financial data use cases. Design and deploy GenAI applications on cloud infrastructure (AWS, Azure, or GCP). Build and optimize knowledge graph and hybrid retrieval architectures to improve grounding and accuracy of LLM outputs. Partner closely with valuations, data, and platform teams to translate financial domain requirements into engineering solutions. Own technical decisions end-to-end, from prototyping through production deployment. Evaluate and integrate emerging LLM and agent frameworks as the platform matures. Required Qualifications 8–13 years of experience building ML/AI systems. 2+ years of hands-on experience with LLMs — RAG, agentic systems, and prompt engineering. Strong hands-on experience with: Python LangChain / LangGraph SQL GenAI deployment on AWS, Azure, or GCP Preferred Qualifications Knowledge graph expertise — Neo4j, Amazon Neptune, or TigerGraph, Cypher, entity resolution, ontology design, and hybrid/Graph-RAG retrieval. Exposure to financial data, valuations, or fund accounting (not required). What We're Looking For The ideal candidate is a builder who's comfortable owning a GenAI system from architecture through production, with real hands-on depth across LLMs, knowledge graphs, and cloud deployment — not just adjacent experience. Financial domain exposure is a bonus, not a requirement. Show more Show less