ML Engineer

Role Description

• Typically, minimum of 4 years professional experience in coding, designing, developing, and analyzing data in a data science setting.
• Create end-to-end ML workflow, training pipelines, engineer new features, monitor training process, and unit testing.
• Coding and architecting of end-to-end applications on modern data processing technology stack: Hadoop, Hive, AWS Cloud, Spark ecosystem technologies.
• Work on analytics application infrastructure analysis & requirements (e.g., configuration, access, tools).

Qualifications

• Experience: 8 to 10 years.
• Technical Skills: Google Cloud - Machine Learning Domain Skills.

Requirements

• Test components of the pipeline and deploy them into a target environment.
• Enable sharing models and collaboration to improve model management with a model registry.
• Define model serving options, create testing pipeline in production for Batch and Real Time models.
• Enable model monitoring for detection of anomalies, early alerts, and triggering ML pipelines for retraining.
• Unify all the processes and automate entire release process with CI/CD orchestration.
• Strong DevOps, Data Engineering, and ML background with AWS.
• Experience with one or more of MLOps tools: ModelDB, Kubeflow, Airflow, Pachyderm, and Data Version Control (DVC).
• Experience in distributed computing, data pipelines, and AI/ML.
• Experience setting up and optimizing databases for production usage in ML application context.
• Experience in Docker, Kubernetes, Jenkins.
• Experience in Spark, Kafka, HDFS, Cassandra.
• Strong Python, Apache Spark, Kubernetes skills.

Company Description