Enterprise Generative AI Engineer (Google Cloud)

Work setup: Remote
Engagement: Long-term, client-facing implementation role
English: Strong professional / fluent (stakeholder-facing)
Role summary
We are looking for an engineer to design, configure, integrate and support secure, production-grade generative-AI solutions built on Google Cloud. The core of the role is turning everyday business processes into scalable, AI-powered workflows — enterprise search, knowledge discovery, question-answering and agent-based automation — on top of Google's enterprise GenAI and search stack (for example Gemini Enterprise, Vertex AI Search and Agent Builder). You will work hand-in-hand with business stakeholders, cloud and platform engineers, security teams and application developers to take solutions from discovery through to live operation.
What you'll do
Solution build & AI agents
Configure and deploy enterprise GenAI applications to match business and customer requirements — data stores, search experiences, actions and agents.
Build AI-driven enterprise search, knowledge discovery, Q&A and workflow-automation capabilities.
Create reusable agents that serve operations, customer service, sales, HR and knowledge-management use cases.
Data integration & retrieval quality
Connect the platform to structured and unstructured company data — Google Workspace, Microsoft 365, databases, document repositories, cloud storage, APIs and third-party business apps.
Design secure ingestion, indexing, synchronisation and retrieval flows, and make sure applications are wired to the right data stores for answers, actions and agents.
Improve answer and search quality through metadata design, document preparation, access controls, grounding and retrieval tuning.
Security & governance
Implement identity, access management and role-based access so retrieval respects each user's existing permissions.
Keep solutions aligned with organisational security, privacy and governance policy, and work with security/compliance teams on data residency, auditability, retention and responsible-AI requirements.
Identify and mitigate risks such as data leakage, prompt injection, unauthorised access, hallucinations and inappropriate responses; maintain documentation of architecture, data flows and access controls.
Deployment & operations
Support solutions across development, testing, production rollout and ongoing operation.
Monitor usage, search quality, agent performance, latency, errors and user feedback; set up logging, monitoring and alerting.
Troubleshoot data connectors, indexing, authentication, API and permission issues, and recommend improvements from adoption metrics and evaluation results.
Stakeholder collaboration
Run discovery sessions to understand requirements, workflows, data sources and desired outcomes, and translate them into technical architecture and delivery plans.
Produce proofs of concept, demos, documentation and technical presentations; train administrators, developers, support teams and end users on capabilities and responsible use.
Must-have qualifications
Bachelor's degree in Computer Science, IT, Engineering or a related field — or equivalent hands-on experience.
3+ years in cloud engineering, software development, data engineering, AI engineering or enterprise application implementation.
Hands-on experience with Google Cloud Platform.
Experience with generative AI, large language models, conversational AI or enterprise-search technologies.
Experience integrating applications with APIs, databases, cloud storage and third-party systems.
Proficiency in at least one programming language — Python preferred (Java, JavaScript/TypeScript or Go also welcome).
Solid grasp of REST APIs, authentication, OAuth, service accounts and identity & access management.
Working knowledge of prompt engineering, retrieval-augmented generation, grounding, embeddings and semantic search.
Familiarity with structured and unstructured data ingestion, plus testing, troubleshooting, logging and monitoring.
Strong analytical, documentation, communication and stakeholder-management skills.
Nice to have
Hands-on delivery with Google's enterprise GenAI products (Gemini Enterprise / Vertex AI Search / Agent Builder) or comparable platforms.
Experience with Vertex AI, the Gemini APIs or other conversational-AI platforms.
Building enterprise AI agents and multi-step agentic workflows.
Integrating Google Workspace or Microsoft 365 data.
BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub and API management.
Document processing, vector search, embeddings and knowledge-management platforms.
Infrastructure as code and CI/CD (Terraform, GitHub Actions, Cloud Build).
Understanding of responsible AI, model evaluation, AI governance and enterprise data privacy.
Prior client-facing consulting, professional-services or enterprise-implementation work.
Relevant Google Cloud certifications (e.g. Professional ML Engineer, Professional Cloud Architect, Professional Data Engineer, Professional Cloud Developer, Generative AI Leader).
Core competencies
Strong problem-solving and analytical thinking.
Ability to translate business needs into practical AI solutions.
Attention to data security, privacy and governance.
Clear technical and business communication.
Comfortable juggling several implementation priorities and working independently while coordinating across teams.