Lead Gemini Enterprise Agent Platform Engineer
Job description
About the role
The role defines detailed technical design for AI search and conversational platforms.
Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.
Key facts
What you'll do
Technical architecture is defined and translated into scalable, production-ready solutions for search and AI initiatives. Enterprise integration patterns are implemented with strong API design and cloud-native approaches.
Development teams are led, and engineering standards are enforced to ensure delivery quality across multidisciplinary groups. Cloud platforms and GCP services are guided to support AI Search implementation patterns and infrastructure decisions.
Technical discovery is driven through estimation sessions and solution design workshops with stakeholders. Risks and dependencies are identified early, and mitigation strategies are proposed to safeguard platform scalability and reliability.
Industrialization of AI and search use cases is supported through hands-on contributions in complex technical areas. Operationalization of solutions is enabled by observability, testing, and security practices aligned with enterprise requirements.
Requirements
Professional software engineering experience of 7+ years is required in enterprise environments. Expertise in Google Cloud Platform and AI ecosystems must be demonstrated clearly.
Experience with AI Search, conversational AI, and generative AI integrations is mandatory. Application architecture and enterprise integration work must show strong API design capabilities.
Understanding of cloud-native, microservices, and event-driven architectures needs to be solid. CI/CD pipelines, software quality, observability, and cloud security practices must be handled proficiently.
Working with Kubernetes and Infrastructure as Code is required. Technical leadership experience, including mentoring engineers and guiding decisions, is essential.
Collaboration across architecture, engineering, cloud, and data teams must be effective. Communication and stakeholder management skills need to serve both technical and non-technical audiences.
Delivery of enterprise-scale digital solutions in Agile environments is a core responsibility. Backend development experience with Java and/or Python is required. Fluency in French and English at an advanced level is necessary.
Practical notes
This is a permanent role offering a competitive compensation package.
Typical interview steps
Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.
Good to know
Data and AI search platforms rely on robust cloud infrastructure and clear architectural patterns. Experience with container orchestration and infrastructure management supports reliable deployments.
Cross-functional collaboration drives successful outcomes in enterprise-scale digital initiatives. Continuous learning and adaptation remain central in fast-evolving technology landscapes.
Career growth
Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.