Principal Solution Architecture AI Native Drug Disc

Johnson & Johnson Johnson & Johnson · Pharma · Beerse, Antwerp, Belgium +2

Principal Solution Architect for an AI-native drug discovery engine, focusing on designing and integrating AI/ML models, agentic tools, and data pipelines within a closed-loop DMTL system. The role involves architecting solutions for data generation, workflow orchestration, and integration across lab systems and enterprise platforms, ensuring alignment with security and regulatory standards.

What you'd actually do

  1. Design and govern modular solution architectures ensuring seamless data and workflow integration. Define integration patterns across platforms such as LIMS, SDMS, data platforms, and AI/ML systems
  2. Architect solutions enabling closed-loop data generation and feedback cycles across discovery workflows. Support design of AI-ready data pipelines, ensuring data quality, lineage, and accessibility and enable integration of AI/ML models and agentic tools into discovery processes
  3. Lead architecture for systems integration across lab automation, laboratory systems, and enterprise platforms, define patterns for event-driven workflows, orchestration, and observability in automated discovery pipelines. Collaborate with engineering teams (cloud, data, ML Ops) to deliver robust, scalable solutions
  4. Partner with scientists, data engineers, AI researchers, and lab automation teams to translate scientific workflows into technical solutions. Co-design solutions that enable AI-augmented science and decision intelligence
  5. Ensure architectures align with cybersecurity, data governance, and regulatory standards. Embed secure-by-design principles, particularly in the context of AI and sensitive discovery data

Skills

Required

  • 5+ years in solution architecture, platform architecture, or systems integration
  • Proven experience delivering complex, distributed, data-driven platforms
  • Cloud-native architectures and distributed systems
  • API-led and event-driven integration patterns
  • Data platforms (e.g., Snowflake or equivalent AI-ready data systems)

Nice to have

  • life sciences / pharma R&D environments
  • AI/ML platforms and MLOps ecosystems
  • Laboratory systems (LIMS, SDMS) and lab automation environments
  • Data engineering and ingestion pipelines
  • Understanding of drug discovery workflows (Design–Make–Test–Learn)
  • Awareness of automation in laboratory environments and data generation pipelines
  • Change Management
  • Coaching
  • Consulting
  • Critical Thinking
  • Emerging Technologies
  • Enterprise IT Governance
  • Information Security Management System (ISMS)
  • Information Technology Strategies
  • Information Technology Trends
  • IT Architecture
  • Organizing
  • Presentation Design
  • Product Configuration
  • Project Reporting
  • Report Writing
  • Requirements Analysis
  • Solution Architecture
  • Technical

What the JD emphasized

  • critical components of an end-to-end AI-native drug discovery engine
  • AI/ML systems
  • AI/ML models and agentic tools
  • AI-augmented science
  • regulatory standards

Other signals

  • designing and delivering critical components of an end-to-end AI-native drug discovery engine
  • scalable, fit-for-purpose end-to-end technology stack supporting a Design–Make–Test–Learn (DMTL) closed-loop system, using AI, automation, and proprietary data to accelerate discovery outcomes
  • integration of AI/ML models and agentic tools into discovery processes
  • event-driven workflows, orchestration, and observability in automated discovery pipelines
  • AI-augmented science and decision intelligence