Manager, GenAI Engineering & Enablement

At Johnson & Johnsonwe believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented treated and curedwhere treatments are smarter and less invasive andsolutions are our expertise in Innovative Medicine and MedTech we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow and profoundly impact health for more at

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Job Description:

Johnson & Johnson is recruiting for a Manager - GenAI Engineering & Enablement for the EMEA Full Stack Engineering chapter located in Beerse Belgium.

Join our dynamic team as a Manager GenAI Engineering & Enablement and become the catalyst behind our next wave of innovation. At TS EMEA Full Stack Engineering (FSE) we build modern secure and scalable software products for our business partnersand were rapidly accelerating the adoption of enterprise GenAI to supercharge developer productivity speed platform delivery and enable responsible innovation at scale across EMEA.

In this role you will support the implementation of the GenAI strategy platform enablement and engineering execution that power our products every day. Youll help apply standards and guardrails that keep us fast and safe partner closely with the global TS GenAI Platform and XENA Developer Experience teams to champion open collaboration and uplift engineering teams through targeted training proven patterns and reusable assets. From discovery and pilot to productization and scaled adoption youll contribute to successful project deliveryembedding responsible AI across the lifecycle and turning great ideas into resilient productionready solutions.

Bring your expertise amplify your impact and help us redefine whats possible. In a culture where creativity thrives and collaboration is the norm youll streamline how we build elevate how our teams deliver and shape the future of our platform and products across EMEA. If youre ready to lead from the front and transform possibilities into realities wed love to have you on the journey.

Key Responsibilities:

• Execute on 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 including guardrails and usage guidelines to balance 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 objectives and key results.

• Showcase impactful use cases and measurable productivity improvements enabled by GenAI adoption.

• Supervise a portfolio of GenAI initiatives ensuring secure privacy-conscious and responsible AI practices throughout the delivery lifecycle.

• Provide technical guidance and unblock teams on critical GenAI engineering challenges while coaching tech leads towards self-sufficiency.

• Foster a collaborative community by connecting squads to enterprise communities of practice and encouraging open participation in platform evolution.

Qualifications

Education:

• We expect the candidate to hold an advanced degree (MSc/PhD) in AI/ML Data Science Computer Science or Applied Mathematics.

Experience and Skills:

Required:

• 8 years in software or platform engineering with experience in team leadership or project management for modern application delivery.

• Handson familiarity with GenAI/LLM systems in productionincluding prompting grounding/RAG evaluation safeguards and telemetry

• Experience with to enterprise AI tooling enablement (e.g. Copilot Intelligent Chat) and broader developerproductivity accelerators.

• A deep understanding of DevOps/MLOps for reliability and lifecycle management (CI/CD for models and services monitoring rollback and drift management).

• Knowledgeable in creating playbooks training programs office hours and innersource 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 nontechnical audiences.

• Proficiency in at least one modern language (e.g. Python TypeScript/ ...) to review designs/code and build prototypes using common frameworks (e.g. FastAPI Flask Express) when needed.

• Production experience with at least one major cloud (Azure AWS or GCP) and container/orchestration stacks (Docker Kubernetes); familiarity with CI artifact registries and 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 contributing to or stewarding open collaboration models (e.g. open meetings contributor guides shared backlogs decision records/ADRs) and cultivating healthy innersource communities.

• Background working with enterprise platforms and regulated environments (e.g. GxP/SaMD) and comfort navigating policy data protection and risk frameworks (e.g. GDPR model risk management).

• Deeper LLMOps capabilities: vector databases and retrieval patterns prompt/version management offline & online evaluation frameworks safety/moderation pipelines and cost/performance tuning (caching quantization).

• Observability for AI systems: tracing evaluation pipelines feedback loops; familiarity with experiment tracking tools.

• Community building: running CoPs hackathons and publishing patterns; recognized internal thought leadership or external speaking/writing.

• Relevant certifications (e.g. Azure AI Engineer AWS ML Specialty GCP ML Engineer); privacy/security credentials (e.g. CIPP/E) are beneficial.

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