Take your career to the next level with Amaris Consulting as an AI Enablement Engineer – Mobile Applications . Become part of an international team, thrive in a global group with €800M turnover and 1,000+ clients worldwide, and an agile environment by planning the kickoff and follow up on projects. Join Amaris Consulting, where you can develop your potential and make a difference within the company.

WHAT WOULD YOU NEED? ✍️

• Bachelor’s degree in Computer Science, Software Engineering, or equivalent
• Proven experience with AI-assisted software engineering workflows, agents, and modern development tooling
• Experience developing mobile applications on iOS and/or Android platforms, including modular architectures and shared platform development
• Experience with QA automation, device testing farms, and CI/CD platforms such as GitHub Actions and Jenkins
• Experience integrating automation, testing, and quality controls into engineering delivery pipelines
• Ability to quickly understand existing engineering ecosystems and work effectively across multiple teams
• Strong collaboration and communication skills in distributed engineering organizations
• Ability to train and support teams adopting new engineering practices, tooling, and workflows
• Strong autonomy and ability to drive initiatives without direct authority

Nice to have:

• Familiarity with LLM-based automation and enterprise AI tooling evaluation/adoption
• Experience improving developer experience, engineering velocity, release quality, or automation maturity

WHAT WILL YOU DO?

• Evaluate and promote AI-assisted engineering tools and workflows aligned with enterprise standards and architecture principles
• Collaborate with the Mobile Platform team, QA, and DevOps teams to identify automation and productivity opportunities across mobile, APIs, integrations, and CI/CD pipelines
• Define and implement AI-assisted engineering practices across mobile development, QA, and delivery pipelines
• Integrate AI-assisted practices into shared frameworks, SDKs, testing utilities, modular architectures, and CI/CD tooling
• Drive adoption of AI-assisted tools, automation, and engineering workflows across development teams
• Support teams with best practices around AI-assisted development, code review, test generation, documentation, and automation
• Define and track engineering productivity and QA metrics, identifying opportunities for continuous improvement
• Promote consistent adoption of AI-assisted practices across teams, contributing to engineering governance and shared standards