Python Developer

Job Description

Python Developer

Onsite Role

Charlotte NC

Key Responsibilities

• Build and maintain large-scale data processing pipelines using Apache Spark for batch and streaming data.

• Design and implement ML training and inference workflows using PyTorch and integrate them into production systems.

• Develop and orchestrate ETL and ML pipelines with Apache Airflow, ensuring reliability, scalability, and observability.

• Optimize performance of data pipelines and ML model training on distributed clusters.

• Collaborate with Data Scientists and ML Engineers to productize models and deploy them into production environments.

• Implement best practices for code quality, CI/CD, unit testing, and monitoring.

• Ensure data quality, integrity, and security across all pipelines.

• Troubleshoot performance bottlenecks and optimize resource utilization.

• Stay up to date with advancements in ML frameworks, distributed computing, and workflow orchestration tools.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

• 5+ years of professional Python development experience, with strong object-oriented programming and software engineering fundamentals.

• Hands-on experience with PyTorch for model training and inference.

• Deep understanding of Apache Spark for distributed data processing (PySpark or Scala is a plus).

• Strong experience with Apache Airflow for workflow orchestration in production environments.

• Proficiency in SQL and working with relational and NoSQL databases.

• Experience with Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).

• Familiarity with data versioning and ML model lifecycle management (MLflow or similar).

• Strong problem-solving and debugging skills in distributed systems.

Preferred Skills

• Experience with real-time data processing frameworks (Kafka, Flink).

• Knowledge of feature stores, data lake architectures, and Delta Lake.

• Familiarity with MLOps practices (CI/CD for ML, model registry, automated retraining).

• Experience with GPU-accelerated ML training and performance optimization.

• Contribution to open-source ML or data engineering projects.

Full-time