Machine Learning Engineer Data Transformation & Cloud Solutions
Only Looking for W2 candidate, H1B also can apply (Transfer)
Duration;12 months
Location: Plano, TX (On-Site)
Responsibilities:
Maintain and monitor existing pipelines and model APIs to ensure optimal functionality.
Work closely with data scientists, machine learning engineers, DevOps, and InfoSec teams to initiate and manage various releases.
Transform legacy code into robust, high-quality Python code that complies with internal coding standards and includes comprehensive test coverage.
Work with the machine learning engineering team to develop and implement robust testing strategies, including unit, integration, and end-to-end tests.
Build and maintain scalable machine learning pipelines on AWS cloud platforms using SageMaker and Snowflake.
Assist in deploying machine learning models into production environments, ensuring scalability, reliability, and performance.
Qualifications:
Proficiency in Python and SQL development.
Experience with modern software development practices, including version control systems and infrastructure-as-code.
Experience building and deploying machine learning models in cloud environments (AWS, Snowflake).
Preferred:
A graduate degree in fields such as Statistics, Mathematics, Physics, Engineering, Computer Science, Economics/Econometrics, Finance, Data Science, Machine Learning, AI, Financial Engineering, Computational Finance, Operational Research, or Industrial Engineering.
Active AWS Solutions Architect Associate certification.
Experience with SAS (statistical analysis) programming language.
Experience working with API frameworks (Flask, FastAPI) and Amazon SageMaker.
Ability to design and implement comprehensive testing strategies.
Apply Now
Duration;12 months
Location: Plano, TX (On-Site)
Responsibilities:
Maintain and monitor existing pipelines and model APIs to ensure optimal functionality.
Work closely with data scientists, machine learning engineers, DevOps, and InfoSec teams to initiate and manage various releases.
Transform legacy code into robust, high-quality Python code that complies with internal coding standards and includes comprehensive test coverage.
Work with the machine learning engineering team to develop and implement robust testing strategies, including unit, integration, and end-to-end tests.
Build and maintain scalable machine learning pipelines on AWS cloud platforms using SageMaker and Snowflake.
Assist in deploying machine learning models into production environments, ensuring scalability, reliability, and performance.
Qualifications:
Proficiency in Python and SQL development.
Experience with modern software development practices, including version control systems and infrastructure-as-code.
Experience building and deploying machine learning models in cloud environments (AWS, Snowflake).
Preferred:
A graduate degree in fields such as Statistics, Mathematics, Physics, Engineering, Computer Science, Economics/Econometrics, Finance, Data Science, Machine Learning, AI, Financial Engineering, Computational Finance, Operational Research, or Industrial Engineering.
Active AWS Solutions Architect Associate certification.
Experience with SAS (statistical analysis) programming language.
Experience working with API frameworks (Flask, FastAPI) and Amazon SageMaker.
Ability to design and implement comprehensive testing strategies.
Apply Now