Software Engineer for AI & Continuous Improvement
Confirmed on the employer's own hiring board on Oct 22, 2026. First seen by Alion on Oct 21, 2026. Qualysoft scores B on the Alion truth index.
Responsibilities
Identify opportunities to improve the team's development, testing, support and delivery processes
Design and implement practical automation solutions that reduce manual effort, improve quality and increase confidence in delivery
Use AI-assisted development and automation tools to improve requirements refinement, testing, documentation, diagnostics and engineering workflows
Evaluate emerging technologies and assess where they can be applied pragmatically within the team
Build practical, working solutions that can be adopted by the team, rather than producing concepts or recommendations alone
Analyse production incidents, identify root causes and turn recurring issues into opportunities for improvement
Promote engineering practices that enable the team to deliver more consistently, efficiently and with stronger controls
Core Skills Required
Experience designing, building and delivering production-grade applications or automation solutions
Strong analytical and problem-solving skills, with the ability to investigate complex systems and data flows
Experience building automated testing frameworks or improving test coverage for existing systems
Practical experience with at least one major programming or scripting language, such as Python, Java or a functional programming language
Basic experience with databases, SQL, data analysis or reporting tools
Comfortable working in a Linux environment and using command-line tools
Familiarity with Jira, Git, code review processes or team documentation tools
Willingness to learn Q/KDB+ and apply it to the team's use cases
Useful Exposure
AI-assisted development, workflow automation, data analysis automation or prompt-based productivity tools
Application architecture and system integration patterns
APIs, interfaces and distributed systems
Databases and data flows
Batch and real-time processing concepts
Production support processes
Financial markets, risk data, trading platforms or regulatory reporting environments