Software Engineer – Developer Infrastructure & AI Tooling

AAA Global; Chicago (IL); On-site; USD 120,000 - 160,000; Full time

Job summary

AAA Global is seeking a software engineer to enhance how engineering teams develop, test, and release software. You will work on developer infrastructure, build systems, and CI/CD platforms, integrating AI-powered tools to streamline workflows across large codebases.

You’ll collaborate with software engineers to identify bottlenecks, implement scalable solutions, and improve development environments in Linux-based setups. Strong communication and problem-solving are essential for success.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering or related technical discipline.
• Advanced Python programming experience.
• Professional experience with C++ and Java.
• Experience developing or supporting developer infrastructure, internal engineering tools, build systems or CI/CD platforms.
• Strong understanding of software build, testing and release workflows.
• Exposure to AI-assisted software development tools.
• Experience configuring or building workflows around AI coding tools is relevant.
• Comfortable working in Linux-based environments.
• Ability to work independently while collaborating with engineering teams.

Responsibilities

• Integrate AI development tools into existing engineering workflows.
• Develop automation around repetitive development, testing, and maintenance tasks.
• Assess new AI engineering products and determine value addition.
• Create shared configurations and workflows for coding assistants.
• Measure tool impact on engineering efficiency and maintain clear documentation.

Skills

Python

C++

Java

CI/CD

Developer Infra

Linux

Problem Solving

Education

Bachelor's degree in CS or related field

Tools

Git

Docker

Job description

Our client is looking for a software engineer to work on the systems and tooling that improve how engineering teams develop, test and release software.

The role sits at the intersection of software engineering and developer infrastructure. You’ll work across build systems, testing frameworks, CI/CD, development environments and internal engineering tools, with a focus on reducing friction and making development workflows faster and more reliable.

An increasing part of the position involves incorporating AI-powered development tools into engineering workflows. This could include deploying coding assistants and agents, automating repetitive engineering tasks, improving how developers interact with large codebases, and assessing emerging AI developer tools.

You’ll work directly with software engineers to identify bottlenecks and build scalable solutions that improve engineering productivity across the organisation.

What We’re Looking For

• Strong software engineering background, ideally with a degree in Computer Science, Computer Engineering or a related technical discipline
• Advanced Python programming experience
• Professional experience with C++ and Java
• Experience developing or supporting developer infrastructure, internal engineering tools, build systems or CI/CD platforms
• Strong understanding of software build, testing and release workflows
• Practical exposure to AI-assisted software development, such as Claude Code, Codex, GitHub Copilot, Cursor or similar coding-agent tooling
• Experience configuring, integrating or building workflows around AI coding tools is particularly relevant
• Comfortable working in Linux-based environments
• Able to work independently while collaborating closely with engineering teams
• Strong problem-solving and communication skills

Candidates from sophisticated engineering environments — including large technology companies, infrastructure-heavy software organisations or quantitative/trading firms — would be particularly relevant. Trading experience itself is not required.

What You Could Work On

• Integrate coding agents and AI development tools into existing engineering workflows
• Develop automation around repetitive development, testing and maintenance tasks
• Assess new AI engineering products and determine where they can add value
• Create shared configurations and workflows for coding assistants
• Measure whether new tools are genuinely improving engineering efficiency

Build & Testing Infrastructure

• Improve build performance and reliability across large codebases
• Develop caching and other infrastructure to reduce build times
• Improve automated testing frameworks and address unreliable or flaky tests
• Analyse build and test bottlenecks and implement improvements

CI/CD & Release Engineering

• Build and improve continuous integration and deployment systems
• Develop automation around code integration and software releases
• Improve pipeline reliability and failure detection
• Reduce manual intervention across the software delivery lifecycle

Developer Environments & Internal Tools

• Automate development environment configuration and provisioning
• Build self-service tools that allow engineers to work more independently
• Develop command-line tools, templates and other internal developer utilities
• Improve consistency between development, testing and production environments
• Work with engineering teams to understand where development time is being lost
• Build tooling that removes recurring pain points
• Establish ways of measuring the effectiveness of developer infrastructure
• Maintain clear documentation and engineering standards