Sage AI is a nimble team within Sage, building innovative services and solutions using generative AI and machine learning to turbocharge our users' productivity. The Sage AI team builds capabilities to help businesses make better decisions through data-powered automation and insights.

We are currently hiring a Principal Machine Learning Engineer to help us build machine learning solutions that will provide insights to empower businesses and help them succeed. As a part of our cross-functional team including data scientists and engineers you will help steer the direction of the entire company's Artificial Intelligence and Machine Learning initiatives.

This is a hybrid role – three days per week in our Atlanta or Lawrenceville office.

If you share our excitement for applying artificial intelligence and machine learning, value a culture of continuous improvement and learning and are excited about working with cutting edge technologies, apply today!

You have:

- Keen interest in artificial intelligence and machine learning and extensive practical experience with it
- Expert knowledge and experience with relevant programming languages (incl. Python), frameworks (incl. OpenAI, HuggingFace, Spark, Azure, AWS)
- Extensive experience with cloud environments (AWS, Azure, GCP)
- Ability to write highly performant code working with big data
- Bachelor's degree, preferably in a field that strongly uses data science / machine learning techniques (e.g. computer science/engineering, statistics, applied math)
- Fluency in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and predictive modeling
- Strong quantitative and analytical skills with significant experience with data science tools
- Ability to communicate complex ideas in machine learning to non-technical stakeholders

You may have:

- Experience with one or more ML Ops frameworks — MLFlow, Kubeflow, Azure ML, Sagemaker
- Strong theoretical foundations in linear algebra, probability theory, or optimization
- Experience and training in finance and operations domains
- Deep experience with ML approaches: deep learning, generative AI, large language models, logistic regression, gradient descent
- Experience wrangling complex and diverse data to solve real-world problems