Associate Director, AI Enablement – Commercial & Medical Affairs

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BeOne Medicines

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10.000+ Mitarbeiter

Gegründet 2010

BeOne Medicines ist ein globales Onkologieunternehmen mit Sitz in der Schweiz, das innovative Therapien entdeckt und entwickelt, die für Krebspatientinnen und -patienten weltweit erschwinglicher und besser zugänglich sind. Mit einem Portfolio, das Hämatologie und solide Tumoren abdeckt, beschleunigt BeOne die Entwicklung seiner vielfältigen Pipeline neuartiger Therapeutika durch interne Kompetenzen und Kooperationen. Mit einem globalen Team von mehr als 11.000 Mitarbeitenden auf sechs Kontinenten setzt sich das Unternehmen dafür ein, den Zugang zu Arzneimitteln für deutlich mehr Patientinnen und Patienten, die sie benötigen, grundlegend zu verbessern.

Beschreibung

• Lead the design and implementation of enterprise AI/ML and GenAI platforms, including RAG pipelines, LLM orchestration layers, and agentic AI frameworks. • Build scalable, reusable AI services and APIs that enable rapid development and deployment of AI use cases across the organization. • Define and implement LLMOps/MLOps practices (model lifecycle management, monitoring, evaluation, versioning, CI/CD). • Architect solutions integrating vector databases, knowledge stores, and enterprise data platforms for context-aware AI applications. • Ensure seamless integration of AI capabilities with existing data, CRM, and marketing technology ecosystems. • Establish frameworks, toolkits, and best practices to enable data scientists, engineers, and analysts to build AI-powered applications efficiently. • Drive self-service AI capabilities, including prompt frameworks, reusable components, and standardized pipelines. • Improve developer productivity and experimentation velocity through well-designed AI abstractions and tooling. • Lead internal adoption of AI platforms through documentation, training, and enablement programs. • Design and operationalize RAG architectures for enterprise knowledge retrieval and grounded generation. • Build and scale agentic AI systems capable of multi-step reasoning, orchestration, and task automation. • Evaluate and implement LLM strategies (fine-tuning vs. RAG vs. hybrid approaches) based on use case needs. • Ensure robustness of GenAI systems through evaluation frameworks, guardrails, and monitoring. • Partner with data engineering teams to ensure high-quality, accessible, and governed data pipelines for AI consumption. • Enable real-time and batch AI use cases through event-driven and streaming architectures. • Optimize performance, scalability, and cost of AI workloads across cloud environments. • Define and enforce AI governance frameworks, including model validation, explainability, and auditability. • Ensure compliance with data privacy, security, and regulatory requirements (e.g., HIPAA, GDPR, GxP where applicable). • Implement safeguards for GenAI risks (hallucination, bias, data leakage). • Act as a bridge between technology, data science, and business teams, enabling scalable AI adoption. • Partner with stakeholders to translate business needs into platform capabilities and reusable solutions (not one-off builds).

🎯 Anforderungen

• Master's degree or higher with 6+ years of experience in AI/ML engineering, platform development, or data engineering, preferably in enterprise environments. • Strong hands-on experience with: RAG architectures and LLM frameworks (e.g., LangChain, LlamaIndex) • Vector databases (Pinecone, FAISS, Weaviate, etc.) • LLMOps/MLOps tooling and production model lifecycle management • Experience building scalable AI platforms, APIs, and microservices architectures. • Proficiency in Python, SQL, and modern cloud platforms (AWS, Azure, or GCP). • Experience with Databricks, Snowflake, or similar data platforms. • Familiarity with distributed systems, real-time architectures, and data pipelines. • Strong understanding of AI system evaluation, monitoring, and optimization. • Proven experience leading platform-centric AI initiatives, not just analytics use cases. • Ability to drive standardization, reuse, and scalability across AI implementations. • Strong collaboration skills across engineering, data science, and business teams. • Experience influencing architecture decisions and driving adoption of shared platforms. • Ability to translate complex AI insights into clear, strategic recommendations for business leaders. • Strong influencing skills, collaborating effectively with stakeholders, and cross-functional teams. • Experience leading AI/analytics initiatives that drive measurable business impact. • Excellent communication and storytelling skills, making AI-driven insights accessible and actionable for executives.

🏖️ Vorteile

• Medical • Dental • Vision • 401(k) • FSA/HSA • Life Insurance • Paid Time Off • Wellness