Gcp Ai Architect

Há 4 dias


Lisboa, Portugal Aptonet Tempo inteiro

GCP AI Architect Location: Lisbon, Portugal We are looking for a Technical Architect to lead the design of innovative Artificial Intelligence and GenAI solutions. Main Responsibilities Lead, together with the Functional Architect, the design process of AI/GenAI solutions, from conception to implementation. Define the Technical Architecture based on project requirements, selecting the ideal components (e.G., Serverless vs. Kubernetes, Cloud SQL vs. NoSQL, Caching, Storage). Ensure the application of best practices in security, cost management, governance, and compliance for AI usage in Public Cloud environments. Orchestrate GenAI solution modules: LLMs, embeddings, vector databases, data ingestion, monitoring, and observability. Lead Proofs of Concept (PoCs) and act as a technical reference for development teams. Actively contribute to reference frameworks, architecture patterns, and technical documentation. Work autonomously to identify and resolve complex technical problems in contexts with multiple stakeholders. Mandatory Requirements Education: Bachelor's degree in Computer Engineering or a similar field. Certification: At least one of the following certifications: Google Certified Professional Cloud Architect or Azure Solutions Architect Expert. Proven Experience: Solid experience designing solutions with Cloud-Native Architectures in Public Cloud, with a clear preference for GCP. Cloud Technical Knowledge: Mastery of GCP technologies (Pub/Sub, Cloud Run, Cloud SQL, GKE, Cloud Storage, etc.). Deep knowledge of Cloud security and application of "security by design" principles. Experience with event-driven architectures and integration strategies. AI/GenAI Knowledge: Experience with Cloud-based LLMs (Azure OpenAI, Vertex AI, or equivalent). Practical knowledge in RAG and Vector Databases (e.G., MongoDB, PostgreSQL, BigQuery Vector Search). Familiarity with GenAI frameworks such as LangChain, LlamaIndex, or Agent Development Kit (ADK). Essential Soft Skills: Excellent communication and leadership skills to drive architecture discussions. Strong autonomy and resilience in highly complex and ambiguous contexts. Nice-to-Have Deep knowledge of integration technologies such as Kafka and Apigee. Experience with Data Mesh ecosystems. Knowledge of security applied to GenAI (e.G., sensitive data controls, prompt injection prevention). Practical experience in MLOps processes and AI model governance.


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