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.
All candidates should make sure to read the following job description and information carefully before applying.
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. xsgimln 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, , or the Anthropic Console
Experience connecting AI agents to enterprise systems and workflows
Infrastructure as code (Terraform)