As an Associate Director, RDU IT AI, youll be at the forefront of Alexions IT RDU organization, reporting directly to the IT Director of Data Science. Your missionTo provide technical leadership and strategic direction in developing and implementing cutting-edge Machine Learning solutions, with a special focus on Generative AI. Your expertise will be pivotal in architecting, scaling, and deploying impactful AI models that drive our business forward.
Accountabilities:
- Lead the development and refinement of Generative AI systems and applications using state-of-the-art models to tackle complex business problems. - Drive the design, development, and continuous improvement of robust infrastructure and tools for Machine Learning and Generative AI models, ensuring scalability and efficiency. - Provide technical oversight for the full lifecycle development of Generative AI agents, ensuring meaningful, reliable, and secure interactions. - Architect and lead the integration of pre-trained Generative AI models into custom applications to enable innovative functionalities. - Collaborate with senior engineers to design interactive systems leveraging Generative AI for dynamic content or decision support. - Build and optimize scalable infrastructure for running Generative AI applications in production, ensuring high performance and reliability. - Oversee the implementation of user interfaces or API endpoints for seamless interaction with Generative AI functionalities. - Design and oversee data pipelines for feeding Generative AI models with high-quality data. - Monitor the operational health and performance of Generative AI applications in production, driving continuous improvement. - Contribute to the development of assets, frameworks, and reusable components for Generative AI, Machine Learning, and MLOps practices. - Collaborate with various collaborators to translate intricate business needs into clear technical requirements. - Support the integration of Generative AI models within the Machine Learning ecosystem, ensuring ethical use and business relevance. - Assist with monitoring and governance of Generative AI models to align with evolving business and regulatory requirements.
Essential Skills/Experience:
- Bachelors degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field - 6+ years of experience in Machine Learning, data science, or software development - Strong understanding and practical experience with advanced ML Engineering concepts and tools, including Docker/containerization, orchestration (e.g., Kubernetes), and robust MLOps practices for end-to-end model lifecycle management - Demonstrated experience designing, implementing, and optimizing robust CI/CD pipelines using tools such as GitHub Actions, Jenkins, or Bitbucket pipelines for automated ML model deployment and continuous integration - Proficiency in Python programming, coupled with deep experience in utilizing and optimizing leading ML libraries such as TensorFlow, Keras, or PyTorch for complex Generative AI applications and research - Experience with cloud computing platforms, such as AWS, Azure or GCP - Detailed understanding of software engineering principles, construction patterns, and established procedures (e.g., modular build, testing, code review), with demonstrated experience in leading or making significant contributions within agile development methodologies - Strong communication and teamwork abilities, adept at simplifying technical concepts for diverse audiences, and encouraging teams to achieve project goals. - Proactive and eager to learn with strong analytical and problem-solving skills
Desirable Skills/Experience:
- Experience in the pharmaceutical industry or related fields - A Masters or equivalent experience in a related field (e.g., Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a closely related quantitative subject area) is required - Proven track record of successfully leading and delivering complex, production-grade Generative AI projects from ideation and research to deployment and operationalization
