Remote ML Engineer for Big Data Analytics & Pipelines

Placements24; Gqeberha; Ibrido; ZAR 900.000 - 1.300.000; Tempo pieno

Vantaggi offerti da questo lavoro

Home office stipend

Health insurance

Flexible hours

Professional development

Descrizione del lavoro

Placements24 is seeking a remote Machine Learning Engineer with 3+ years of experience to design, train, and deploy scalable ML models on big data platforms. You will work with a globally distributed team to build end-to-end ML solutions.

You''ll implement data pipelines, leverage Spark/Hadoop, and apply MLOps practices in a cloud environment while collaborating across data science and software engineering teams.

Competenze

• 3+ years of professional ML engineering or data science experience.
• Experience with big data tech such as Spark or Hadoop.
• Proficiency in Python or Scala and ML libraries like TensorFlow/ PyTorch.
• Experience with cloud ML services and CI/CD for ML workflows.

Mansioni

• Develop, train, and deploy ML models using large-scale datasets.
• Design data pipelines for ML training and inference.
• Utilize Spark/Hadoop for data processing and feature engineering.
• Implement MLOps practices for versioning, monitoring, and deployment.
• Collaborate with data scientists and software engineers to productionize models.
• Optimize algorithms and data processing for performance and scalability.
• Evaluate models and identify improvements, staying current with ML trends.
• Document models, pipelines, and deployment steps.

Conoscenze

Big data

Spark

Python

Scala

ML libraries

Remote collaboration

Formazione

Bachelor''s or Master''s in CS/Data Science

Strumenti

Apache Spark

Hadoop

CI/CD for ML

Descrizione del lavoro

Placements24 is seeking a remote Machine Learning Engineer with 3+ years of experience to design, train, and deploy scalable ML models on big data platforms. You will work with a globally distributed team to build end-to-end ML solutions.

You''ll implement data pipelines, leverage Spark/Hadoop, and apply MLOps practices in a cloud environment while collaborating across data science and software engineering teams.