Python Developer

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.