Principal AI Engineer — AI Solutions; Enabling Functions

AI Builder Role

Are you the hands-on AI builder who turns sophisticated governance into code and ships production systems that stand up to regulators, auditors, and real users? Can you move from idea to working proof-of-concept in days, then mature it into a resilient, governed application that powers how a global business hires, contracts, spends, and stays compliant?

Based in Barcelona, you will be the technical authority within a high-impact squad, partnering closely with engineering leadership, enterprise technology, data engineering, legal, security, and functional teams across HR, Finance, Procurement, Legal, Audit, Compliance, and Business Development. Your work will deliver auditable, compliant AI from the first commit—helping colleagues operate faster and safer, and ultimately accelerating how we bring life-changing medicines to patients.

In this builder-first role, you design multi-agent architectures, build RAG pipelines over real enterprise corpora, route models across regions to respect sovereignty requirements, and ship governed applications into live environments. You will set engineering standards by example while remaining the most prolific builder on the team.

Accountabilities:

• Technical Design and Solution Architecture:
  Lead the end-to-end technical design for AI solutions across enabling functions—selecting patterns, defining component boundaries, and making build-versus-integrate decisions aligned to the squad's architectural direction.
• High-Consequence Decisions:
  Make and document model strategy choices (foundation models, fine-tuning, multi-provider orchestration, RAG), integration patterns, and tooling with clear rationale against product requirements and compliance constraints.
• Platform and Reuse:
  Identify and build shared components, reusable agent patterns, and governance instrumentation that speed delivery across the portfolio—championing platform thinking over one-off projects.
• Data Sovereignty in Code:
  Translate sovereignty policy into working infrastructure—implementing model routing, data residency, and hosting decisions that respect jurisdictional boundaries and cross-border transfer requirements.
• Hands-On Multi-Agent Delivery:
  Design and ship multi-agent LLM systems across providers, write the orchestration code, implement sovereignty-aware routing, and ensure safe outcomes with human-gated control where needed.
• Production RAG Pipelines:
  Build governed RAG pipelines with hallucination guards before any user-facing output and immutable audit logging at every stage—from ingestion through to response.
• Rapid Prototyping with Production Intent:
  Deliver POCs in days with clear progression gates to pilot and production, using real data and defined success criteria.
• Governance-as-Code:
  Implement classification/tiering logic, model registration, approval workflows, monitoring pipelines, decommissioning controls, human-in-the-loop oversight, deterministic routing layers, model/data cards, and full audit trails as first-class engineering components.
• Multi-Jurisdictional Compliance:
  Engineer systems that meet data protection laws, AI-specific regulations (e.g., EU risk classifications), and sector-specific operational resilience expectations—coding for divergence to remain defensible under multiple regimes.
• Operational Resilience:
  Bake resilience into architecture with circuit breakers, provider fallbacks, graceful degradation, fail-safe defaults, tested failover paths, and chaos/resilience testing to defined tolerances.
• Delivery and MLOps:
  Own delivery from design to production, scaling, monitoring, and lifecycle management; establish CI/CD, automated testing, performance monitoring, incident response automation, and capacity management.
• Data Engineering and Controls:
  Build pipelines, feature stores, and data products for quality, governance, and reuse; implement data quality, lineage, and controls for financially material data, legally privileged documents, employee-sensitive information, and supplier-confidential data.
• Model Quality and Assurance:
  Implement drift and bias monitoring, lead AI red-teaming, embed fairness and explainability pipelines, manage technical risks, and…