Job description

Minimum Requirements:

  • Matric (Grade 12)
  • Masters degree in Software Engineering, Data Engineering, Computer Science or related field
  • 5 years of relevant work experience
  • Strong Scala and Python background
  • Experience with Apache Spark and/or Ray
  • Knowledge of AWS, GCP, Azure, or other cloud platform
  • Knowledge of current principles and frameworks for ML Ops
  • Experience with ML Ops technologies such as ML Flow, DVC, Grafana, DataHub, Databricks
  • Experience with machine learning technologies such as PyTorch, TensorFlow, AWS Sagemaker
  • Experience with CI/CD pipelines, including Jenkins or Git Actions
  • Experience with Docker containerization or Kubernetes orchestration
  • Experience in improving data security and privacy, and managing and reducing cloud costs
  • Knowledge of API development and machine learning deployment


Responsibilities:

  • Develop and implement a strategy for continuous improvement of our Machine Learning Ops including versioning, testing, automation, reproducibility, deployment, monitoring, and data privacy
  • Develop and report on ML Ops metrics such as deployment frequency, lead time for changes, mean time to restore, and change failure rate
  • Collaborate with data scientists, data engineers, API engineers, and the dev ops team
  • Build scalable data ingestion and machine learning inference pipelines
  • Scale up production systems to handle increased demand from new products, features, and users
  • Provide visibility into the health of our data platform (comprehensive view of data flow, resources usage, data lineage, etc) and optimize cloud costs
  • Automate and handle the life-cycle of the systems and platforms that process our data

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