Description

You will build and maintain scalable data pipelines, data warehouses, and infrastructure to support machine learning workflows.

Responsibilities

• Build and maintain scalable data pipelines and data warehouses.
• Develop ETL processes and automate data pipelines with a focus on performance and scalability.
• Engineer features and preprocess data to support ML training and inference.
• Implement MLOps best practices for continuous integration, deployment, and monitoring of models in production.
• Optimize data infrastructure for low-latency access and high throughput for real-time ML applications.

Required Skills

• 5+ years of experience in data engineering.
• 2-3 years of experience in machine learning engineering or deploying ML models in production.
• Proficiency in Python and SQL with experience in data query optimization.
• Hands-on experience with Apache Spark, Hadoop, Kafka, and Airflow.
• Expertise in data modeling and ETL processes.
• Experience with cloud platforms (AWS, GCP, or Azure) and services like AWS Glue, BigQuery, or Azure Data Lake.
• Experience with data storage solutions such as Snowflake, Redshift, or BigQuery.
• Knowledge of containerization and orchestration using Docker and Kubernetes.
• Familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-Learn.
• Experience with model lifecycle tools such as MLflow, TFX, or Databricks.
• Understanding of data versioning, reproducibility, and model monitoring.

Preferred Skills

• Hands-on experience with NLP, computer vision, or time-series data.
• Knowledge of data governance, quality, security, and data lineage.

Key Skills

Education

Any Graduate