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.