# Director AI Enablement Lead at Novartis

Novartis is hiring a Director AI Enablement Lead. It pays €65k-€121k a year and Level rates it Works on AI ●●●○; you can [apply on Novartis](https://jobsbylevel.com/go/c586d762-f946-4cd6-8310-0183fca7b9d0?utm_medium=ai-agent).

AI Level 3, AI centrality 71 out of 100. Barcelona Gran Vía.

## Details

- Company: [Novartis](https://jobsbylevel.com/companies/novartis)
- AI level: AI Level 3 (score 71 out of 100)
- Location: Barcelona Gran Vía
- Salary: €65k-€121k
- Posted: November 10, 2026
- Apply: https://jobsbylevel.com/go/c586d762-f946-4cd6-8310-0183fca7b9d0?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=director-ai-enablement-lead-at-novartis-cb0e0f

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## Description

Salary Range: €65,200.00 - €121,000.00 Job Description Summary #LI-Hybrid Location: Barcelona, Spain Job Description Summary The AI Enablement & Delivery Lead is responsible for defining and executing AI capabilities for the Statistical Compute Environment, related Domino ecosystem and broader Novartis AI landscape. The role translates business and scientific priorities, Domino product strategy and the Novartis AI landscape into secure, compliant, scalable and production-ready capabilities. It owns delivery from architecture and engineering through adoption and measurable value realization and serves as the technical design and delivery authority across cross-functional teams and vendors. Working across platform engineering, data science, enterprise AI, security, architecture and business teams, the role integrates the Statistical Compute Platform with the Novartis AI ecosystem, establishes secure and reusable enablement patterns and turns prioritized use cases into production-ready AI solutions. Job Description Key Responsibilities Define and execute the AI roadmap for the Statistical Compute Environment and related ecosystem, translating Domino product strategy, AQS priorities and the Novartis AI landscape into a prioritized delivery plan and production-ready capabilities. Own end-to-end delivery of AI platform capabilities , spanning engineering, security, compliance, production readiness, adoption and measurable business value realization. Define the end-to-end GenAI architecture , from cloud model providers such as AWS Bedrock through enterprise LLM gateways to the applications and use cases that consume them. Design secure, identity-aware access , including authentication, authorization and traceability, ensuring model usage is secure, auditable and attributable to each user and use case. Establish standards and reusable onboarding patterns , defining appropriate guardrails, logging, model access and shared platform services that enable teams to deliver AI use cases quickly and consistently. Act as the technical design authority , driving and documenting key architecture decisions across internal teams, platform vendors and cloud providers and taking designs through enterprise architecture, security and compliance reviews. Enable production scale , defining requirements for resilience, performance, observability, validation, security and cost management so AI solutions can progress from proof of concept to validated production in a regulated GxP environment. Evaluate, onboard and enable AI tools and services , assessing emerging Domino and enterprise AI capabilities and integrating selected services into the QUANTA and broader Novartis AI ecosystems. Translate business and scientific requirements into delivered solutions , prioritizing use cases and platform investments, selecting appropriate GenAI and data science techniques and driving implementation and adoption. Shape the future AI capability portfolio , identifying high-value opportunities for the quantitative science organization and translating selected innovations into roadmap investments. Essential Requirements Significant experience designing and delivering enterprise AI, GenAI or data science platforms and capabilities at scale. Strong expertise in AI platform architecture, including cloud-hosted models, LLM gateways, platform services and production applications. Strong knowledge of security, identity and governance, including authentication, authorization, traceability, responsible AI and compliance controls. Proven experience taking AI solutions from proof of concept through to scalable, production-ready implementation. Practical expertise in GenAI and data science techniques, including prompt and context engineering, Retrieval-Augmented Generation (RAG), fine-tuning, model training and evaluation. Experience defining reusable technical standards, shared services and onboarding patterns that enable AI adoption across multiple teams. Experience working

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