Machine Learning Engineer
Machine Learning Engineer
We are looking for a skilled Machine Learning Engineer to join a high-performing team focused on modernizing data science models and migrating legacy code to a cloud-based infrastructure. In this role, you will leverage Python, AWS SageMaker, and Snowflake to develop, test, and deploy machine learning models and model pipelines. You will play a crucial role in transforming legacy SAS code into high-quality Python, building scalable systems, and ensuring that models perform optimally in production environments. Your expertise in software development practices, cloud infrastructure, and testing strategies will drive the success of model releases and retraining activities.
Key Responsibilities:
- Transform legacy SAS code into robust, efficient Python code, ensuring it meets internal coding standards and includes comprehensive test coverage.
- Collaborate with the machine learning team to design and implement testing strategies, including unit, integration, and end-to-end tests.
- Build and maintain scalable machine learning pipelines on AWS cloud platforms using tools like SageMaker and Snowflake.
- Deploy machine learning models into production, ensuring they meet performance, scalability, and reliability requirements.
- Continuously monitor and optimize model APIs and pipelines for operational efficiency.
- Work cross-functionally with data scientists, DevOps, InfoSec, and other stakeholders to facilitate successful model deployments and system integrations.
- Translate complex technical requirements into clear documentation and effective communication with upstream and downstream teams.
Key Requirements:
- 4-5 years of experience in software development (60%) and data science (40%).
- Strong proficiency in Python and SQL development.
- Experience with modern cloud platforms, specifically AWS and Snowflake, and familiarity with AWS services such as SageMaker.
- Proven track record of transforming legacy systems, particularly migrating SAS code to Python.
- Experience designing and implementing testing strategies for machine learning pipelines and models.
- Familiarity with version control systems, CI/CD pipelines, and infrastructure-as-code practices.
- Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders.
- Experience with API frameworks (Flask, FastAPI) and a strong understanding of deployment practices.
Preferred Qualifications:
- Graduate degree in a quantitative discipline such as Statistics, Mathematics, Physics, Engineering, or Data Science (or equivalent experience).
- Active AWS Solutions Architect Associate certification.
- Experience with SAS programming and statistical analysis.
- Experience building and deploying machine learning models in cloud environments.
- Familiarity with the latest machine learning techniques and best practices for production environments.
Why Apply?
If you are passionate about developing cutting-edge machine learning models, transforming legacy systems, and working with modern cloud technologies, this is the opportunity for you. Join a collaborative team where your contributions will directly impact the future of data science and machine learning in a high-growth environment. We encourage you to apply today with your resume and contact information. We look forward to hearing from you!