Associate Director, AI Enablement (Commercial & Medical Affairs)

BeiGene

3d•$144,300 - $194,300•Onsite

About The Position

The Global Commercial and Medical Affairs Technology team at BeOne is seeking an Associate Director, AI Enablement to lead the design, build, and scaling of enterprise AI platforms and capabilities that power next-generation analytics and intelligent applications. This role will focus on engineering and operationalizing AI systems—including Retrieval-Augmented Generation (RAG), agentic AI frameworks, and LLM-powered services—enabling teams across Commercial, Medical, and other functions to build and deploy AI solutions in a secure, scalable, and reusable manner. As a key technology leader, you will define and drive the AI platform strategy, developer experience, and enablement frameworks, ensuring seamless integration with BeOne’s modular technology stack (data platforms, CRM, marketing systems, and analytics environments). You will partner closely with data science, engineering, and business teams to transition AI from experimentation to enterprise-grade, production-ready capabilities. Our team operates at the intersection of data, AI, and engineering, with a strong focus on reusable architecture, rapid iteration, and measurable impact.

Requirements

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.
Business Acumen & Leadership: 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.

Nice To Haves

Experience in pharmaceutical or healthcare industry.
Exposure to commercial, medical, or patient data ecosystems.
Experience with agentic AI frameworks and autonomous systems design.

Responsibilities

AI Platform & Architecture: 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.
AI Enablement & Developer Experience: 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.
GenAI, RAG & Agentic Systems: 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.
Data & AI Engineering: 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.
Governance, Security & Compliance: 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).
Cross-functional Leadership: 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).
Influence enterprise AI strategy and roadmap through deep technical expertise and pragmatic execution.