Design and implement distributed data pipelines using Apache Spark and Databricks
Develop and deploy machine learning models for low-latency, real-time inference
Construct and manage CI/CD pipelines for machine learning infrastructure
Integrate and orchestrate asynchronous workflows and real-time data streams

Requirements

Bachelor’s degree in Computer Science, Information Technology, Computer Engineering, Data Science, or related field plus 2 years of related experience
Design and implement distributed data pipelines using Apache Spark and Databricks to ingest, transform, and store large volumes of structured and unstructured data across multi-stage environments
Develop and deploy machine learning models for low-latency, real-time inference using Amazon SageMaker and containerized services, including the implementation of bring-your-own-container (BYOC) strategies and endpoint optimization
Construct and manage CI/CD pipelines for machine learning infrastructure using tools such as ArgoCD, Helm, and GitHub Actions to automate model deployment, rollback, and lifecycle reproducibility across staging and production environments
Integrate and orchestrate asynchronous workflows and real-time data streams using Apache Kafka, AWS Lambda, and Step Functions to enable feature computation, message-driven processing, and scalable ML inference pipelines

Benefits

Applicant Tracking System Keywords

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

Hard Skills & Tools

Apache SparkDatabricksmachine learningCI/CD pipelinesAmazon SageMakercontainerized servicesArgoCDHelmGitHub ActionsApache Kafka