Principal, Artificial Intelligence Engineering

What You’ll Do

We've recently formed a dedicated AI Research & Engineering team, and we're looking for a Principal AI Engineer to serve as its technical anchor.

This is a senior individual contributor role by design. You won't manage people; you'll do the work, set the technical standard, and lead through expertise. You'll be the primary driver of agentic AI capabilities, architecting and building the systems that connect AI agents to real enterprise workflows in one of the most consequential and carefully regulated environments in financial markets. You'll shape AI roadmap alongside the Executive Director of AI Engineering, mentor junior engineers, and have time for experimentation with frontier tools. Our team works directly with AWS and Anthropic, accessing Claude models through both.

Primary Duties and Responsibilities:

To perform this job successfully, an individual must be able to perform each primary duty satisfactorily.

Partner with the Executive Director of AI Engineering to define and execute AI technical roadmap, translating organizational priorities into concrete architectural decisions

Lead agentic AI efforts: architect, build, and operate systems that connect AI agents to internal systems, data pipelines, and business workflows

Leverage direct relationships with AWS and Anthropic to evaluate and adopt new model capabilities and tooling as they become available

Build and ship production AI applications on AWS using Claude and related Anthropic tooling, maintaining high standards for reliability, security, and auditability

Architect scalable systems that integrate LLMs and AI agents into internal systems, operational workflows, and business processes, owning decisions from design through production deployment

Serve as the primary technical mentor for junior engineers: conducting code and architecture reviews, modeling engineering best practices, and raising the team's overall technical level (no direct reports)

Define and enforce AI safety standards appropriate for a regulated SIFMU environment, including hallucination detection, output validation, bias assessment, and audit trails that satisfy SEC and CFTC oversight expectations

Establish responsible AI practices and safety guardrails for LLM applications operating on sensitive financial data

Navigate change management and security review processes when deploying AI systems into production

Evaluate emerging AI technologies and provide concrete, risk-aware recommendations on adoption

Qualifications:

The requirements listed are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the primary functions.

Required

Experience mentoring and elevating junior engineers

Preferred

Exceptional ability to explain complex technical decisions to non-technical stakeholders

Background in financial market infrastructure, derivatives, or similarly regulated industries

Education and/or Experience:

Bachelor's or master's in computer science or a related technical field

10 years of software engineering and systems architecture experience, with demonstrated technical leadership

5 years as a senior individual contributor on complex, high-stakes production systems

Technical Skills:

Required

Expert-level Python; proficient in SQL

Deep system design expertise: distributed systems, microservices, event-driven architectures, APIs, and MCP servers

Hands-on experience with data engineering: pipelines, transformation, and data modeling

AWS experience; comfort with Docker, Kubernetes, and CI/CD pipelines

Strong production AI/LLM experience, or demonstrated hands-on passion for the space with the engineering depth to ramp quickly

Strong working knowledge of AI risk vectors: hallucinations, prompt injection, bias, data privacy, and output validation

Preferred

Production experience building and operating LLM-powered applications

Experience designing and operating agentic AI systems in enterprise environments

Experience with context engineering strategies including RAG, prompt architecture, and retrieval pipeline design

Familiarity with frontier models including Claude, GPT, and Gemini, and hands-on experimentation with emerging tools

Hands-on experience with Anthropic's tooling and APIs, including the Claude API, Claude.ai, or the Anthropic Console

Experience connecting AI agents to enterprise systems and workflows

Infrastructure as code (Terraform)