Harvard Business Publishing (HBP) aims to enhance the value delivery and interactivity of its product offerings through the application of AI and ML technologies. The AI Developer will drive innovation by supporting product teams in developing new AI-enabled features, facilitating technical interoperability, and developing/maintaining ML and AI infrastructure.
Requirements
- 5+ years in data science, ML/AI development, or a closely related field, with a proven track record of delivering production-grade AI/ML solutions
- Hands-on experience with developing, deploying, and maintaining ML infrastructure, including model training, evaluation, and production-ready APIs.
- Familiarity with MLOps tools and concepts including MLFlow, AWS Sagemaker, model registries, and CI/CD.
- Experience delivering AI/ML APIs using FastAPI, flask, or similar.
- Excellence in Python programming and experience with relevant data science and AI/ML packages such as pandas, scikitlearn, pytorch, and Langchain.
- Extensive experience in collaborative development with git
- Practical experience with cloud services such as AWS (preferred), Azure, or Google Cloud for deploying and managing AI/ML workflows and containerization tools like docker
Responsibilities
- Support product teams in building new AI-enabled features
- Facilitate technical interoperability
- Develop and maintain ML and AI infrastructure
- Contribute to data engineering
- Support data science and analytics
- Work closely with HBP product teams to develop and iterate on prototypes quickly
- Ensure that AI and ML capabilities are effectively integrated into new product ideas
Other
- Excellent communication and stakeholder management skills, with the ability to work effectively across multiple teams and departments.
- A strong passion for innovation and the ability to think creatively about how AI and ML can be applied to solve business problems and enhance product offerings
- 3+ years of experience developing products with, and guardrails for, LLMs strongly preferred
- Experience support end users of no-code AI-assistive tools and platforms and ability to support transitions of no-code prototypes to dev and production environments
- Familiarity with data engineering concepts and tools, including data pipelines, ETL processes, data warehousing, the medallion architecture, and dbt
