Lead Developer, AI-Assisted Engineering Practices (Geneva)

About the role

This is a hands-on role for someone who has turned AI-assisted development from ad hoc usage into a documented, production-grade practice. The role combines building the practice within the delivery team with driving adoption across other teams through standards, tooling, pairing, and knowledge transfer. The environment is regulated, with real legacy, strong governance, and experienced engineers.

Responsibilities

• Work hands-on inside the delivery team to build a production-grade AI-assisted development practice, starting with automated code review agents that check code quality, security, and adherence to standards.
• Extend the practice to automated testing (generated and maintained unit and integration suites with real regression coverage) and to documentation that stays current rather than being written once and abandoned.
• Map how teams develop today, then propose where to start - which project, what it would prove, what it would cost - and present that to both engineers and management before executing it.
• Make the practice adoptable by other teams through written standards, tuned tooling, pairing, workshops, and transfer to internal engineers.
• Configure and tune review agents until teams trust them as a step ahead of or alongside human review.
• Shape where the practice extends next: specification support, design and architecture, AI-paired coding, CI/CD and release orchestration, and the feedback loop from production back into development.
• Operate within regulated enterprise constraints: approved tooling, network controls, audit expectations, and governance over what code and data may be sent to external models

Experience

• At least 10 years of software development, including senior or lead responsibility
• Delivery experience in large corporate environments, with the constraints that come with them: governance, legacy, shared platforms, and change management
• Proven hands-on experience integrating AI-assisted development into production engineering workflows and delivering production-grade solutions.
• Able to walk through work you built yourself in depth: instruction files and prompts, agent or skill configuration, review rules and how you tuned them, generated artefacts, and the measured effect on the delivery process
• Proven ability to elevate the quality and effectiveness of a team through technical expertise, collaboration, and influence, including with experienced engineers.

Technical fluency

• Java, in depth - the main back-end stack (required)
• Python for tooling, automation, and the AI-assisted delivery chain itself (required)
• Solid command of delivery discipline: code quality, testing strategy, CI/CD, release and delivery governance
• Experience with an orchestrated development framework (e.g. GStack or equivalent) is a plus, but not essential.
• Comfortable building a production-grade practice inside a regulated enterprise environment, including approved tooling, network controls, and audit expectations
• Familiarity with Angular and the ability to review front-end implementations are a plus

Ways of working

• Able to articulate and defend technical positions with expert teams and communicate effectively with management.
• Bring a structured approach and strong working discipline, alongside technical expertise.
• Clearly distinguish between hypotheses and verified findings.
• Comfortable starting in ambiguous situations and defining a practical path forward.
• Structured and detail-oriented, with the confidence to hold a clear position under ambiguity or challenge.

Qualifications

• Track record of designing or building AI-assisted engineering practices in a large, complex, regulated organisation (finance, insurance, health, energy, or public sector a plus)
• Fluent French and written technical English
• Eligibility to work in Switzerland

Your data

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