Work closely with Machine Learning Engineers to understand, refine, and prioritize requirements
Design and build our model serving service with simple, powerful APIs to capture our users/clients needs
Build our core ML model lifecycle management system to provide an ML-aware release and deployment experience
Improve machine learning data quality by using & building tools to automatically detect issues
Create intelligent ML-aware real-time monitoring & observability systems
Work closely with partner teams to integrate with other ML tools to create a seamless end-to-end experience
Leverage open-source technologies like Kubeflow, Kubernetes, Spark, Docker, Airflow, Tensorflow, and PyTorch

Requirements

Strong coding skills in Python/Scala or equivalent
Strong experience with LLM and large sets of data
Experience with distributed data processing tools like Hive, Spark, Airflow and popular ML frameworks like Tensorflow or Pytorch are preferred
Solid understanding of engineering best practices and complexities of models in production
Experience developing and productionizing machine learning models is a plus
Experience with Docker, Kubernetes, and Spark is a plus
Experience with distributed data processing tools like Hive, Spark, and Airflow is a plus
Industry experience building end-to-end Machine Learning Infrastructure is a big plus

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonScalaMachine LearningLLMData ProcessingModel Lifecycle ManagementModel DeploymentData Quality ImprovementReal-time MonitoringObservability

Soft Skills

CollaborationRequirement PrioritizationProblem SolvingCommunication