Artificial Intelligence AI LLM Engineer

Responsibilities: Architect agent orchestration patterns, tool and context integration, and evaluation pipelines for reliability in a regulated environmentAI infrastructure supporting agents and applications on AWS, and contribute to the team's growing infrastructure self-sufficiency Implement technical safeguards against AI-specific risks, including hallucination detection, prompt injection defense, and output validation, in collaboration with AI Governance function Build audit logging and human-in-the-loop checkpoints into agentic workflows so agent actions remain traceable and reviewable in a regulated environment Mentor Associate and Senior Associate engineers through code and architecture review Contribute technical point of view to the team's evolving engineering standards and practices, in partnership with the Executive Director

Qualifications: Experience mentoring or elevating the work of junior engineers Demonstrated ownership of production systems from design through deployment Ability to work effectively in a still-forming, fast-moving team environment This position is not eligible for employment be legally authorized to work in the United States without the need for sponsorship now or in the future. Bachelor's degree in Computer Science, Engineering, or a related technical field; Master's preferred 4-7 years of software engineering experience, with production AI/LLM systems experience preferred

Required: Strong Python; proficient SQL; solid distributed systems and API design fundamentals

Required: Hands-on experience building LLM-powered applications or agentic systems

Required: Working knowledge of AI-specific risk areas (prompt injection, output validation, hallucination mitigation, data privacy), or demonstrated ability to build this expertise quickly

Required: Cloud infrastructure experience (AWS preferred); comfort with Docker, CI/CD, and containerization fundamentals

Preferred: Direct experience with Claude, the Anthropic API, or Claude.ai/Console

Preferred: Experience designing agent orchestration or Model Context Protocol (MCP)-based integrations

Preferred: Production experience operating LLM-powered applications or agentic systems at scale

Preferred: Kubernetes and infrastructure-as-code (Terraform)

Preferred: Background in financial services, derivatives, or other regulated industries

Preferred: Exposure to AI security practices such as red-teaming or adversarial testing of model inputs and outputs

Preferred: Data engineering experience (pipelines, data modeling), particularly for candidates interested in AI infrastructure work