AI Software Delivery Specialist, Singapore

What You'll Be Doing Review existing codebases to determine where AI-assisted tools can be effectively applied, taking into account architecture, language mix, and code complexity Explore and benchmark AI solutions that support different stages of the engineering lifecycle, such as automated documentation, test creation, and code quality analysis Run practical experiments using AI tools on selected components to assess:
Output quality and accuracy Test coverage and completeness Time savings compared to traditional methods
Work closely with engineering teams to:
Identify suitable candidates for pilot initiatives Validate AI-generated outputs Feed insights into broader engineering practices
Contribute to the rollout of AI-enabled engineering practices, including:
Defining success criteria Establishing guardrails and review processes Highlighting cost-benefit trade-offs
Investigate how AI capabilities can be embedded into existing development workflows and delivery pipelines Evaluate how ready the organization is to adopt AI-driven practices, and communicate findings to both technical and non-technical stakeholders What We're Looking For Core Experience
Solid background in software engineering (typically 6+ years) Practical exposure to AI-powered development tools in a professional setting Experience assessing and trialing new technologies in structured environments, with clear evaluation criteria Familiarity with applying AI to:
Generate or enhance technical documentation Support automated testing efforts
Strong understanding of software delivery practices and where automation can add value without compromising quality Working knowledge of unit testing approaches and frameworks across common stacks (e.g., .NET or JavaScript ecosystems) Ability to balance technical possibilities with real-world constraints such as team maturity and operating models Confident communicator, able to translate technical findings into meaningful insights for stakeholders Awareness of considerations around data usage, security, and intellectual property when leveraging AI tools Additional Advantage
Exposure to large language model (LLM) use cases in engineering, such as code interpretation or rules extraction Experience working with modern front-end frameworks or full-stack environments Familiarity with tools that assist in automated code review or quality checks Background in enterprise-grade platforms or ecosystems (e.g., Microsoft stack) Understanding of prompt design and optimisation for technical use cases Experience with behaviour-driven or acceptance-driven testing approaches Awareness of techniques that improve AI context handling (e.g., retrieval-based approaches) Exposure to vendor/tool evaluation from a security or compliance standpoint Contributions to internal or external knowledge sharing on AI in engineering
ST Reg No. R1768414 BeathChapman Pte Ltd Licence no. 16S8112