AI Architect (Cloud & Generative AI)

Description du poste:

JOB DESCRIPTION:

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.

The base pay for this position is N/A
In specific locations, the pay range may vary from the range posted.

JOB FAMILY:
Product Development

DIVISION:
ADC Diabetes Care

LOCATION:
Spain > Barcelona : Av. Diagonal, 601

ADDITIONAL LOCATIONS:

WORK SHIFT:
Standard

TRAVEL:
No

MEDICAL SURVEILLANCE:
Not Applicable

SIGNIFICANT WORK ACTIVITIES:
Not Applicable