Senior AI Delivery Engineer

Qube Research & Technologies•New York, NY

38d•$180,000 - $300,000

About The Position

Qube Research & Technologies (QRT) is seeking a Senior AI Delivery Engineer to work closely with engineers, quantitative researchers, and data scientists to accelerate software delivery using modern AI coding tools and agents. This is a hands-on engineering role focused on solving real problems, building practical solutions, and helping teams deliver high-quality software to production faster. The role offers significant autonomy and will be measured by the impact delivered. The engineer will partner with teams to identify opportunities, use AI coding agents to move ideas from specification to production, build targeted tooling, integrations, and automation to improve engineering workflows, and work across teams and domains as priorities evolve. A key aspect of the role involves applying AI-assisted development to accelerate software delivery while maintaining quality, integrating AI agents into existing workflows (CI/CD, code review), establishing pragmatic guardrails and human oversight for AI-assisted development, and sharing best practices. The engineer will own delivery from problem definition to production deployment, balancing speed, quality, and reliability in a fast-paced trading environment, communicating effectively with stakeholders, and driving outcomes with autonomy and a bias for execution.

Requirements

Significant software engineering experience within a quantitative hedge fund, systematic trading firm or similarly demanding engineering environment.
Strong programming skills in one or more modern languages, with the ability to learn new technologies quickly.
Hands-on experience using AI coding agents or LLMs to deliver production software.
Proven ability to solve ambiguous problems and deliver practical solutions independently.
Comfortable working across unfamiliar codebases, teams and technical domains.
Experience with CI/CD, code review workflows and human-in-the-loop development practices.
Strong engineering judgement, including balancing rapid delivery with long-term maintainability.
Excellent communication and collaboration skills.
Demonstrated ownership, pace and delivery of measurable business impact.

Nice To Haves

Experience with market data, real-time systems or trading infrastructure.
Experience building deployment pipelines or AI-assisted execution workflows.
Experience building or self-hosting LLM infrastructure.
Familiarity with Kafka or similar streaming technologies.
Familiarity with authentication, permissions and access control systems (e.g. OIDC).
Contributions to open-source projects.

Responsibilities

Partner with engineering and research teams to identify high-value opportunities and deliver practical solutions.
Use AI coding agents to take ideas from specification through to production.
Build targeted tooling, integrations and automation to improve engineering workflows.
Work across teams, codebases and technical domains as priorities evolve.
Apply AI-assisted development to accelerate software delivery while maintaining engineering quality.
Integrate AI agents into existing development workflows, including CI/CD and code review.
Establish pragmatic guardrails and human oversight for AI-assisted development.
Share reusable approaches and best practices across engineering teams.
Own delivery from problem definition through production deployment.
Balance speed, quality and reliability in a fast-paced trading environment.
Communicate effectively with technical and non-technical stakeholders.
Drive outcomes with autonomy, sound judgement and a bias for execution.