Senior ML Engineer - Data Pipeline & Operations

Placements24; Redmond (WA); Hybrid; USD 55,000 - 91,000; Full time

Benefits offered by this job

Health benefits

Annual leave

Provident fund

Stock options possible

Job summary

Placements24 is seeking a Senior Machine Learning Engineer in Welkom, SA, to design and maintain scalable data pipelines and MLOps workflows for production ML systems. You will collaborate with data scientists, optimize pipelines for cost and performance, and ensure data quality and compliance using Spark, Airflow, and cloud services.

The role requires 3–5 years in data or ML engineering, strong Python/SQL skills, and distributed computing experience.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• 3–5 years of experience in data or ML engineering.
• Proficient in Python and SQL.
• Experience with data pipeline tools (Airflow/Prefect/Dagster).
• Experience with Spark and cloud data services.

Responsibilities

• Design, build, and maintain scalable data pipelines for ML models.
• Ingest, clean, transform, and engineer features from large datasets.
• Develop and manage MLOps workflows for training, deployment, and monitoring.
• Collaborate with data scientists to ensure data quality and requirements.
• Optimize pipelines for performance, cost, and reliability.
• Implement monitoring and alerting for data pipelines and ML models.

Skills

Python

SQL

Airflow/Prefect/Dagster

Spark

Cloud platforms

MLOps

Docker

Data quality

Communication

Teamwork

Education

Bachelor's degree in CS/Engineering

Tools

Apache Airflow

Prefect

Dagster

Apache Spark

Docker

Job description

About the Role

Our client, a dynamic startup leveraging AI to revolutionize the agricultural sector, is seeking a Senior Machine Learning Engineer with expertise in data pipelines and operations. This role, based in Welkom, Free State , is crucial for building and maintaining the robust data infrastructure required to train and deploy our advanced machine learning models. You will be responsible for designing, implementing, and optimizing data ingestion, transformation, and feature engineering pipelines, ensuring high-quality, reliable data for our AI systems. The ideal candidate possesses a strong blend of software engineering skills, data engineering experience, and a solid understanding of machine learning principles.

Key Responsibilities

• Design, build, and maintain scalable and reliable data pipelines for machine learning models.
• Implement data ingestion, cleaning, transformation, and feature engineering processes.
• Develop and manage MLOps workflows for model training, deployment, and monitoring.
• Collaborate with data scientists to understand data requirements and ensure data quality.
• Optimize data pipelines for performance, cost, and efficiency.
• Implement monitoring and alerting systems for data pipelines and ML models.
• Automate data-related tasks and processes to improve operational efficiency.
• Work with large datasets and distributed computing frameworks (e.g., Spark).
• Ensure data integrity, security, and compliance with relevant regulations.
• Stay up-to-date with the latest technologies and best practices in data engineering and MLOps.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related quantitative field.
• 3-5 years of experience in data engineering or machine learning engineering.
• Proficiency in Python and SQL.
• Experience with data pipeline tools such as Apache Airflow, Prefect, or Dagster.
• Experience with distributed computing frameworks like Apache Spark.
• Familiarity with cloud platforms (AWS, Azure, GCP) and their data services.
• Understanding of ML concepts and model deployment strategies.
• Experience with containerization technologies (Docker) is a plus.
• Strong problem-solving and analytical skills.
• Excellent communication and teamwork abilities.

Benefits

• Our client offers a competitive salary, along with comprehensive health and wellness benefits.
• Employees receive a generous annual leave allowance and opportunities for professional development, including training and conference attendance.
• A provident fund and performance-based incentives are also part of the compensation package.
• Our client is committed to fostering a positive work environment and promoting work-life balance.
• Stock options may be available for key personnel.