Development Engineer

LanceSoft is a leading staffing firm in the US, and they are seeking a Development Engineer to support the modernization of demand forecasting capabilities within a digital fulfillment organization. This role involves bridging ML research and production by scaling data processing workloads, building robust ML pipelines, and ensuring reliable forecasting models at scale.

Responsibilities:

• Experience building and deploying ML models in production environments
• Hands-on experience with time series forecasting (Prophet, ARIMA, or similar)
• Understanding of hyperparameter tuning, model validation, and experiment tracking
• Familiarity with feature engineering and feature store concepts
• Proficiency converting pandas-based workloads to PySpark for large-scale processing
• Experience with distributed data processing frameworks (Spark, Dask, or Ray)
• Ability to optimize data pipelines for performance and cost efficiency
• Working knowledge of data formats (Parquet, CSV) and partitioning strategies
• Experience with BigQuery or similar analytical databases (table design, partitioning, clustering, writing/validating datasets)
• Experience building ML pipelines using Kubeflow Pipelines (KFP), Vertex AI, or Airflow
• Understanding of pipeline component design, DAG orchestration, and caching strategies
• Ability to integrate data validation, model training, and deployment steps into workflows
• Experience with pipeline parameterization and configuration management
• Strong Python proficiency with production-grade coding standards
• Ability to read, refactor, and extend existing codebases
• Version control experience (Git) and structured change management
• Familiarity with testing frameworks (pytest), dependency management (Poetry/UV), and code quality tools (pre-commit, linting)
• Hands-on experience with GCP (Vertex AI, Cloud Storage) or equivalent cloud platforms
• Familiarity with containerization (Docker) and container orchestration (Kubernetes)
• Experience with CI/CD pipelines for ML workflows
• Understanding of secrets management and environment configuration
• Experience with Ray for distributed ML training and inference
• Exposure to Hadoop ecosystem tools (Hive, HDFS, Spark on YARN)
• Knowledge of ML model monitoring and drift detection
• Experience with infrastructure-as-code (Terraform, Cloud Deployment Manager)
• Familiarity with retail, supply chain, or demand forecasting domains
• Experience working with data science teams to productionize research code
• Background in scaling ML systems from prototype to enterprise-grade deployments

Requirements:

• Experience building and deploying ML models in production environments
• Hands-on experience with time series forecasting (Prophet, ARIMA, or similar)
• Understanding of hyperparameter tuning, model validation, and experiment tracking
• Familiarity with feature engineering and feature store concepts
• Proficiency converting pandas-based workloads to PySpark for large-scale processing
• Experience with distributed data processing frameworks (Spark, Dask, or Ray)
• Ability to optimize data pipelines for performance and cost efficiency
• Working knowledge of data formats (Parquet, CSV) and partitioning strategies
• Experience with BigQuery or similar analytical databases (table design, partitioning, clustering, writing/validating datasets)
• Experience building ML pipelines using Kubeflow Pipelines (KFP), Vertex AI, or Airflow
• Understanding of pipeline component design, DAG orchestration, and caching strategies
• Ability to integrate data validation, model training, and deployment steps into workflows
• Experience with pipeline parameterization and configuration management
• Strong Python proficiency with production-grade coding standards
• Ability to read, refactor, and extend existing codebases
• Version control experience (Git) and structured change management
• Familiarity with testing frameworks (pytest), dependency management (Poetry/UV), and code quality tools (pre-commit, linting)
• Hands-on experience with GCP (Vertex AI, Cloud Storage) or equivalent cloud platforms
• Familiarity with containerization (Docker) and container orchestration (Kubernetes)
• Experience with CI/CD pipelines for ML workflows
• Understanding of secrets management and environment configuration
• Experience with Ray for distributed ML training and inference
• Exposure to Hadoop ecosystem tools (Hive, HDFS, Spark on YARN)
• Knowledge of ML model monitoring and drift detection
• Experience with infrastructure-as-code (Terraform, Cloud Deployment Manager)
• Familiarity with retail, supply chain, or demand forecasting domains
• Experience working with data science teams to productionize research code
• Background in scaling ML systems from prototype to enterprise-grade deployments