Associate Director, Artificial Intelligence and Machine Learning
Alnylam Pharmaceuticals
- Cambridge, MA
- Permanent
- Full-time
- Lead the design, development, and delivery of AI and Machine Learning solutions focused on NLP, ML and Robotic Process Automation, ensuring the adoption of cutting-edge techniques and technologies, using pertinent AWS, Google and other cloud technologies.
- Execute (prioritized) AI portfolio projects, reinforcing technology innovation, and responsible AI practices and governance throughout the project lifecycle.
- Proactively identify opportunities where AI and ML can deliver business results. Develop and integrate AI capabilities with our existing systems and business workflows.
- Keep abreast of latest developments in the field of AI and ML and incorporate these advancements into our organization.
- Conduct deep data and model analysis on Large Language Models to derive the optimal solutions for business insights and to make strategic decisions.
- Collaborate with other leaders and Business, IT stakeholders to drive AI initiatives, meeting Alnylam’s strategic objectives.
- Promote a culture of continuous learning and knowledge sharing in emerging AI and Machine Learning technologies.
- Liaise with vendors and service providers to select the products or services that best meet Alnylam goals.
- Select and implement the appropriate tools, software, applications, and systems to support AI & ML goals.
- Bachelor’s degree in computer science, Engineering, Data Science, Statistics, or a related field. Master’s degree or PhD preferred.
- Preferred experience in the pharmaceutical or healthcare industry.
- Minimum of 5 years of hands-on experience in Python programming.
- Minimum of 1 year of expertise in designing and implementing AI and ML algorithms using Python.
- Hands on experience working with open-source LLM/CV models and platforms/methologies such as Hugging Face, RAG, LangChain and AWS Bedrock.
- Experience in the complete ML development lifecycle, utilizing frameworks like Pytorch, Tensorflow, and Lightning.
- Solid understanding of other deep learning architectures, including CNN and RNN.
- Experience in deploying ML models using AWS services, notably SageMaker, EC2, Lambda, among others.
- Demonstrable expertise in vector databases, with practical experience in tools like Pinecone and AWS Kendra.
- Proven project management experience and ability to take ownership of challenges from start to finish, demonstrating a willingness to learn and adapt to ensure the success of projects.
- High capacity for multitasking, adeptness at managing multiple projects simultaneously, strong prioritization skills, and a knack for navigating ambiguous situations with ease.
- Good knowledge of applicable data privacy and regulatory practices and laws in biopharma.