Responsibilities
Help design, build, and maintain the infrastructure that powers ML solutions
Manage data pipelines, streamline model deployment, and optimize compute resources
Work on high-impact systems such as ranking, recommendation, and pricing optimization
Collaborate closely with data scientists to integrate models into production
Design and implement scalable ML infrastructure for training, deployment, and serving in batch and real-time environments
Build and maintain efficient data pipelines for large-scale processing and feature engineering
Optimize compute resources and improve model serving performance across ML systems
Implement robust monitoring, logging, and alerting systems
Contribute to ML Ops practices including CI/CD pipelines
Research and integrate new technologies, mentor junior engineers, and communicate technical solutions to diverse stakeholders
Requirements
Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field
6-7 years of experience in Software, Data, or ML engineering roles (preferred)
Strong problem-solving skills
Proficiency in Java and desirable Python
Understanding of ML algorithms, model architectures, and experience building scalable, reliable ML systems
Exposure to cloud platforms (e.g., AWS), containerization (Docker), and scalable data systems (e.g., Spark, Kafka)
Familiarity with CI/CD tools (e.g., GitHub Actions), ML model serving technologies (e.g., MLflow)
Ability to collaborate well across teams
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Help design, build, and maintain the infrastructure that powers ML solutions
Manage data pipelines, streamline model deployment, and optimize compute resources
Work on high-impact systems such as ranking, recommendation, and pricing optimization
Collaborate closely with data scientists to integrate models into production
Design and implement scalable ML infrastructure for training, deployment, and serving in batch and real-time environments
Build and maintain efficient data pipelines for large-scale processing and feature engineering
Optimize compute resources and improve model serving performance across ML systems
Implement robust monitoring, logging, and alerting systems
Contribute to ML Ops practices including CI/CD pipelines
Research and integrate new technologies, mentor junior engineers, and communicate technical solutions to diverse stakeholders
Requirements
Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field
6-7 years of experience in Software, Data, or ML engineering roles (preferred)
Strong problem-solving skills
Proficiency in Java and desirable Python
Understanding of ML algorithms, model architectures, and experience building scalable, reliable ML systems
Exposure to cloud platforms (e.g., AWS), containerization (Docker), and scalable data systems (e.g., Spark, Kafka)
Familiarity with CI/CD tools (e.g., GitHub Actions), ML model serving technologies (e.g., MLflow)
Ability to collaborate well across teams
#J-18808-Ljbffr
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