AI Architect (Cloud & Generative AI)

Location: Spain - Barcelona

Category: Research & Development

Employment Type: Full time

Role Summary

We are hiring a hands-on AI Architect to design and deliver cloud-based Generative AI solutions across Diabetes Care products and internal enterprise workflows. This role blends modern cloud architecture with practical GenAI engineering: you will define reference architectures, build working prototypes, and guide teams to production with secure, scalable, and cost-efficient patterns.

You will drive GenAI productization: move prototypes from PoC to production with clear quality gates, scalability, security, cost controls, and measurable business outcomes.

You will help define and evolve the GenAI tech stack, including Retrieval-Augmented Generation (RAG), context engineering, and vector stores, to ensure reliable grounding and safe operation.

This role is AI-first: you are expected to use AI tools in your daily work to accelerate delivery while maintaining engineering rigor, traceability, and quality.

What You'll Do

• Own end-to-end GenAI solution architecture: data ingestion, retrieval, context assembly, model/agent logic, evaluation, deployment, and monitoring.
• Design, build, and optimize RAG systems (chunking/indexing, embeddings, vector stores, hybrid retrieval, re-ranking) with strong grounding and citation patterns.
• Lead context engineering: prompt templates, structured outputs, tool/function calling, memory/state patterns for agents, and defenses against prompt injection and data leakage.
• Build scalable services and APIs (e.g., FastAPI/Flask) and integrate MCP servers to connect GenAI to tools, data, and enterprise systems.
• Define cloud platform patterns for GenAI workloads (networking, IAM, secrets, observability, resiliency) using modern DevOps and Infrastructure-as-Code.
• Add observability for GenAI services: distributed tracing, structured logs, metrics (latency, cost, quality), dashboards, and alerting.
• Implement evaluation-driven development: golden datasets, automated checks, prompt/agent regression tests, and human review where appropriate.
• Establish LLMOps/GenAIOps practices: versioning (prompts/configs/models), CI/CD, monitoring (latency, cost, quality), and incident response for AI services.
• Partner with security, legal, compliance, quality, and product stakeholders to translate requirements into safe-by-design solutions; mentor engineers and set standards.

Required Qualifications

• Strong cloud architecture experience (AWS/Azure/GCP), including security, networking, IAM, and scalable service design.
• Hands-on GenAI/LLM experience delivering solutions beyond notebooks (OpenAI/Azure OpenAI, AWS Bedrock, or similar).
• Proven experience implementing RAG systems, vector stores, and context engineering for reliable grounding.
• Strong Python engineering (clean code, debugging, testing discipline) and ability to ship prototypes quickly.
• Experience building production APIs/services and integrating with enterprise systems.
• DevOps and CI/CD experience (GitHub Actions and/or Bitbucket pipelines), including automated testing and quality gates.
• Comfortable using coding models to accelerate delivery (e.g., OpenAI Codex, Claude Code, or similar), while maintaining code quality, security, and traceability.
• Strong understanding of GenAI reliability and safety (hallucination mitigation, uncertainty handling, secure model usage, prompt injection awareness).
• Excellent communication and documentation skills for technical and non-technical audiences.

Preferred Qualifications

• Experience with agentic systems (routing, orchestration, multi-step plans, workflow/state management) and common frameworks or equivalent internal tooling.
• Experience with vector databases/search platforms (OpenSearch, pgvector/Postgres, Pinecone, Weaviate, Redis) and hybrid retrieval patterns.
• Experience deploying cloud solutions that integrate with mobile applications and device ecosystems (iOS/Android) and/or enterprise identity (SSO).
• Experience building/operating ML/AI platforms (feature pipelines, training/inference services, MLflow, SageMaker/Vertex/Databricks) and knowing when fine-tuning is appropriate.
• Experience working in regulated environments (PII/PHI controls, auditability, traceability) and scaling solutions across multiple products.

Success looks like:

• Reusable reference architectures and templates for GenAI services adopted across teams.
• Validated prototypes transitioned to production with clear go/no-go criteria and measurable quality.
• Improved reliability, safety, and cost-efficiency of GenAI features across products and internal workflows.