Senior AI Engineer

Geneva Trading•Chicago, IL

6d•Onsite

About The Position

Geneva Trading is a technology-driven proprietary trading firm where engineering directly impacts performance. We're looking for a senior engineer who can lead the adoption of AI across the organization. Someone who stays on the cutting edge, experiments relentlessly, and ships the practices that actually work to the rest of the team. This is a hands-on IC position for someone who thrives on autonomy, moves fast, and measures their success by how much more effective they make everyone around them.

Requirements

Strong software engineering fundamentals; engineer first with AI as a current focus area.
Proficiency in Python and comfort building production systems.
Experience applying ML/AI in practical, production settings (not just research).
Familiarity with LLMs, prompt engineering, and the current AI tooling ecosystem.
Ability to work independently, set priorities, and manage ambiguity.
Strong communication skills to explain work to engineers and senior leadership.
Curiosity and pace; genuine enjoyment in keeping up with a fast-moving field.

Nice To Haves

Experience with agent orchestration frameworks and multi-step AI workflows.
Familiarity with emerging standards and protocols in the AI tooling space (e.g., MCP).
Background in trading, fintech, or other performance-sensitive environments.
Experience with cloud platforms (AWS, GCP, or Azure).

Responsibilities

Explore and evaluate new models, tools, frameworks, and techniques in the AI landscape.
Identify specific problems across the engineering organization, prototype AI-driven solutions, and measure their impact on outcomes.
Package successful AI solutions into workflows, tooling, and best practices for broader team adoption, writing clean, production-quality code.
Define and track metrics related to engineering efficiency, such as cycle time, developer throughput, and time-to-resolution, to demonstrate measurable improvement.
Drive AI adoption through training, documentation, and collaboration, earning trust by consistently delivering results.
Integrate LLMs and AI assistants into engineering workflows (code review, development, testing, documentation).
Build automation and orchestration for repetitive or high-friction processes.
Develop internal tools that leverage AI to improve decision-making and productivity.
Evaluate and deploy new AI capabilities as they emerge.