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.