AI Architect (Cloud & Generative AI)

<p style="text-align:inherit">&nbsp; &nbsp; &nbsp;</p><h2><b>JOB DESCRIPTION:</b></h2><h1>Role Summary</h1><p>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.</p><p>You will drive GenAI productization: move prototypes from PoC to production with clear quality gates, scalability, security, cost controls, and measurable business outcomes.</p><p>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.</p><p>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.</p><h1>What You&#39;ll Do</h1><ul><li>Own end-to-end GenAI solution architecture: data ingestion, retrieval, context assembly, model/agent logic, evaluation, deployment, and monitoring.</li><li>Design, build, and optimize RAG systems (chunking/indexing, embeddings, vector stores, hybrid retrieval, re-ranking) with strong grounding and citation patterns.</li><li>Lead context engineering: prompt templates, structured outputs, tool/function calling, memory/state patterns for agents, and defenses against prompt injection and data leakage.</li><li>Build scalable services and APIs (e.g., FastAPI/Flask) and integrate MCP servers to connect GenAI to tools, data, and enterprise systems.</li><li>Define cloud platform patterns for GenAI workloads (networking, IAM, secrets, observability, resiliency) using modern DevOps and Infrastructure-as-Code.</li><li>Add observability for GenAI services: distributed tracing, structured logs, metrics (latency, cost, quality), dashboards, and alerting.</li><li>Implement evaluation-driven development: golden datasets, automated checks, prompt/agent regression tests, and human review where appropriate.</li><li>Establish LLMOps/GenAIOps practices: versioning (prompts/configs/models), CI/CD, monitoring (latency, cost, quality), and incident response for AI services.</li><li>Partner with security, legal, compliance, quality, and product stakeholders to translate requirements into safe-by-design solutions; mentor engineers and set standards.</li></ul><h1>Required Qualifications</h1><ul><li>Strong cloud architecture experience (AWS/Azure/GCP), including security, networking, IAM, and scalable service design.</li><li>Hands-on GenAI/LLM experience delivering solutions beyond notebooks (OpenAI/Azure OpenAI, AWS Bedrock, or similar).</li><li>Proven experience implementing RAG systems, vector stores, and context engineering for reliable grounding.</li><li>Strong Python engineering (clean code, debugging, testing discipline) and ability to ship prototypes quickly.</li><li>Experience building production APIs/services and integrating with enterprise systems.</li><li>DevOps and CI/CD experience (GitHub Actions and/or Bitbucket pipelines), including automated testing and quality gates.</li><li>Comfortable using coding models to accelerate delivery (e.g., OpenAI Codex, Claude Code, or similar), while maintaining code quality, security, and traceability.</li><li>Strong understanding of GenAI reliability and safety (hallucination mitigation, uncertainty handling, secure model usage, prompt injection awareness).</li><li>Excellent communication and documentation skills for technical and non-technical audiences.</li></ul><h1>Preferred Qualifications</h1><ul><li>Experience with agentic systems (routing, orchestration, multi-step plans, workflow/state management) and common frameworks or equivalent internal tooling.</li><li>Experience with vector databases/search platforms (OpenSearch, pgvector/Postgres, Pinecone, Weaviate, Redis) and hybrid retrieval patterns.</li><li>Experience deploying cloud solutions that integrate with mobile applications and device ecosystems (iOS/Android) and/or enterprise identity (SSO).</li><li>Experience building/operating ML/AI platforms (feature pipelines, training/inference services, MLflow, SageMaker/Vertex/Databricks) and knowing when fine-tuning is appropriate.</li><li>Experience working in regulated environments (PII/PHI controls, auditability, traceability) and scaling solutions across multiple products.</li></ul><p></p><p><b>Success looks like:</b></p><ul><li>Reusable reference architectures and templates for GenAI services adopted across teams.</li><li>Validated prototypes transitioned to production with clear go/no-go criteria and measurable quality.</li><li>Improved reliability, safety, and cost-efficiency of GenAI features across products and internal workflows.</li></ul><p style="text-align:inherit">&nbsp; &nbsp; &nbsp;</p><p><b>The base pay for this position is </b></p>N/A<p style="text-align:left"><span>In specific locations, the pay range may vary from the range posted.</span></p><p style="text-align:inherit">     </p><h2><b>JOB FAMILY:</b></h2>Product Development<p style="text-align:inherit">&nbsp; &nbsp; &nbsp;</p><h2><b>DIVISION:</b></h2>ADC Diabetes Care<p style="text-align:inherit">&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;</p><h2><b>LOCATION:</b></h2>Spain &gt; Barcelona : Av. Diagonal, 601<p style="text-align:inherit">&nbsp; &nbsp; &nbsp;</p><h2><b>ADDITIONAL LOCATIONS:</b></h2><p style="text-align:inherit">&nbsp; &nbsp; &nbsp;</p><h2><b>WORK SHIFT:</b></h2>Standard<p style="text-align:inherit">&nbsp; &nbsp; &nbsp;</p><h2><b>TRAVEL:</b></h2>No<p style="text-align:inherit">&nbsp; &nbsp; &nbsp;</p><h2><b>MEDICAL SURVEILLANCE:</b></h2>Not Applicable<p style="text-align:inherit">&nbsp; &nbsp; &nbsp;</p><h2><b>SIGNIFICANT WORK ACTIVITIES:</b></h2>Not Applicable<p style="text-align:inherit">&nbsp; &nbsp; &nbsp;</p><p style="text-align:inherit">     </p><p style="text-align:inherit">     </p>