Manager, GenAI Engineering & Enablement – Johnson & Johnson Innovative Medicine, Beerse, Belgium (EMEA).
Full‐time, Engineering and Information Technology. EMEA Full Stack Engineering chapter.
Overview
Johnson & Johnson is recruiting for a Manager in GenAI Engineering & Enablement for the EMEA Full Stack Engineering chapter located in Beerse, Belgium. This role will support the implementation of the GenAI strategy, platform enablement, and engineering execution that power our products across EMEA.
Leading the adoption of enterprise GenAI to supercharge developer productivity, speed platform delivery, and enable responsible innovation at scale.
Key Responsibilities
Execute the defined GenAI strategy and roadmap for TS EMEA Full Stack Engineering, ensuring alignment with enterprise platforms and compliance requirements.
Ensure adherence to governance practices, guardrails and usage guidelines, balancing innovation speed with regulatory and policy alignment.
Translate and embed open collaboration models (e.g., XENA) into GenAI workstreams, fostering clear contribution and decision‐making processes.
Partner with platform engineering to standardise and enhance GenAI platform touchpoints, promoting reusable assets and preventing duplication.
Develop and curate reference implementations and templates for engineering teams, supporting consistent AI delivery.
Lead comprehensive GenAI upskilling and training programmes, ensuring inclusive access for all EMEA locations and measuring adoption through OKRs.
Showcase impactful use cases and measurable productivity improvements enabled by GenAI adoption.
Supervise a portfolio of GenAI initiatives, ensuring secure, privacy‐conscious, responsible AI practices throughout the delivery lifecycle.
Provide technical guidance and unblock teams on critical GenAI engineering challenges, while coaching tech leads toward self‐sufficiency.
Foster a collaborative community by connecting squads to enterprise communities of practice and encouraging open participation in platform evolution.
Qualifications
Education
Advanced degree (MSc/PhD) in AI/ML, Data Science, Computer Science, or Applied Mathematics.
Required Experience & Skills
8+ years in software or platform engineering with team leadership or project management experience for modern application delivery.
Hands‐on familiarity with GenAI/LLM systems in production – prompting, grounding/RAG, evaluation, safeguards, telemetry.
Experience with enterprise AI tooling enablement (e.g., Copilot, Intelligent Chat) and broader developer‐productivity accelerators.
Deep understanding of DevOps/MLOps for reliability and lifecycle management (CI/CD for models, monitoring, rollback, drift management).
Knowledgeable in creating playbooks, training programs, office hours, and inner‐source libraries; establishing guardrails for responsible AI use across multiple teams.
Comfortable operating across product, security, platform, legal/privacy, and domain leadership; able to translate sophisticated AI concepts for executive and non‐technical audiences.
Proficiency in at least one modern language (Python, TypeScript/Node.js, etc.) to review designs/code and build prototypes using common frameworks.
Production experience with at least one major cloud (Azure, AWS, or GCP) and container/orchestration stacks (Docker, Kubernetes); familiarity with CI, artifact registries, basic IaC.
Excellent problem‐solving skills and ability to quickly adapt to new technologies and programming languages.
Strong communication and collaboration skills, with the ability to work effectively in team environments.
Fluency in English is required; other languages are beneficial.
Preferred Experience & Skills
Experience contributing to or stewarding open collaboration models and cultivating healthy inner‐source communities.
Background working with enterprise platforms and regulated environments (GxP/SaMD) and comfortable navigating policy, data protection and risk frameworks (GDPR, model risk management).
Deeper LLMOps capabilities – vector databases, retrieval patterns, prompt/version management, offline/online evaluation frameworks, safety/moderation pipelines, cost/performance tuning.
Observability for AI systems – tracing, evaluation pipelines, feedback loops; familiarity with experiment tracking tools.
Community building – running CoPs, hackathons, publishing patterns; recognized internal thought leadership or external speaking/writing.
Relevant certifications (Azure AI Engineer, AWS ML Specialty, GCP ML Engineer); privacy/security credentials (CIPP/E) are beneficial.
Required Skills
Analytical Reasoning, Coaching, Continuous Integration & Continuous Deployment (CI/CD) Pipeline, Critical Thinking, Human‐Computer Interaction (HCI), Information Technology (IT) Infrastructure, Information Technology Strategies, Innovation, Organizing, Presentation Design, Process Improvements, Software Development Management, System Integration, Systems Analysis, Technical Credibility, Technical Writing, Workflow Automation.
#J-18808-Ljbffr