Role and responsibilities

Project Management (50%)
Front Door (Requirements, Metadata collection, classification security clearance)
Data pipeline template development
Data pipeline Monitoring development support (operations)
Design, develop, deploy, and maintain production-grade scalable data transformation, machine learning and deep learning code, pipelines; manage data and model versioning, training, tuning, serving, experiment and evaluation tracking dashboards.
Manage ETL and machine learning model lifecycle: develop, deploy, monitor, maintain, and update data and models in production.
Build and maintain tools and infrastructure for data processing for AI/ML development initiatives.

Requirements

Technical skills requirements
The candidate must demonstrate proficiency in,
Experience deploying machine learning models into production environment.
Strong DevOps, Data Engineering and ML background with Cloud platforms
Experience in containerization and orchestration (such as Docker, Kubernetes)
Experience with ML training/retraining, Model Registry, ML model performance measurement using ML Ops open source frameworks.
Experience building/operating systems for data extraction, ingestion and processing of large data sets
Experience with MLOps tools such as MLFlow and Kubeflow
Experience in Python scripting
Experience with CI/CD
Fluency in Python data tools e.g. Pandas, Dask, or Pyspark
Experience working on large scale, distributed systems
Python/Scala for data pipelines
Scala/Java/Python for micro-services and APIs
HDP, Oracle skills Sql; Spark, Scala, Hive and Oozie DataOps (DevOps, CDC)

Additional Information

Nice-to-have skills
Jenkins, K8S
Google Cloud certification
Unix or Shell scripting