Python Developer

RAPID EAGLE INC; North Carolina; On-site; USD 130,000 - 180,000; Full time

Job summary

RAPID EAGLE INC. in Charlotte, NC is seeking an experienced Python Developer to build and maintain large-scale data processing pipelines and ML workflows in production. The role focuses on distributed systems, modern ML tooling, and cloud-ready backends.

You will collaborate with Data Scientists and ML Engineers to productionize models, implement CI/CD, and ensure data quality, observability, and security across pipelines in an onsite environment.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 5+ years of professional Python development experience with strong OOP fundamentals.
• Hands-on experience with PyTorch for model training and inference.
• Deep understanding of Apache Spark for distributed data processing (PySpark or Scala 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.

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.

Skills

Python development

PyTorch

Apache Spark

Airflow

SQL

Distributed systems

Cloud platforms

ML lifecycle management

CI/CD

Debugging

Education

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

Tools

Docker

Kubernetes

Cloud Platforms (AWS/GCP/Azure)

Job description

Job Description

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.