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AI Architect (Cloud & Generative AI)

Abbott Laboratories -
Barcelona, Barcelona provincia

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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.

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