Senior Data Engineer I

Boardroom Appointments; Gqeberha; On-site; ZAR 900,000 - 1,300,000; Full time

Job summary

Boardroom Appointments seeks a seasoned ML Ops professional to lead end-to-end ML Ops strategy, including versioning, testing, automation, deployment, and data privacy. You will report on deployment metrics, collaborate with data scientists, data engineers, API engineers, and DevOps to build scalable data pipelines.

The role scales production systems for new products and features while optimizing cloud costs and data security.

Qualifications

• Master's 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

Skills

Scala

Python

ML Ops

API development

ML deployment

Data security

Cost optimization

CI/CD

Docker

Kubernetes

Education

Masters degree

Tools

Apache Spark

Ray

AWS

GCP

Azure

Databricks

MLFlow

DVC

Grafana

DataHub

TensorFlow

PyTorch

SageMaker

Jenkins

GitHub Actions

Job description

• 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