LanceSoft, Inc. · 1 week ago
Development Engineer
United States
Full-time
Remote
Senior Level
$83.33/hr - $90.90/hrLanceSoft 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.
Information Technology
No H1B
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
Qualification
Required
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
Preferred
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
Benefits
Four options of medical Insurance
Dental and Vision Insurance
401k Contributions
Critical Illness Insurance
Voluntary Permanent Life Insurance
Accident Insurance
Other Employee Perks
At LanceSoft, full time regular employees who work a minimum of 30 hours a week or more are entitled to the following benefits
Company
LanceSoft, Inc.
Established in 2000, LanceSoft is a pioneer in delivering top-notch Global Workforce Solutions and IT Services to a diverse clientele.
Founded in 2000
5001-10000 employees
Funding
Current Stage
Late StageLeadership Team
Ram Karuppusamy
Chairman & CEO
Simon Dealy
Chief Financial Officer
Recent News
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