Machine Learning Engineer

Ranger Technical Resources; Town of Florida (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Ranger Technical Resources seeks an AI/ML Engineer to turn governed business data into production ML solutions in a Databricks environment. You will own the full ML lifecycle from feature engineering through retraining and deployment, delivering forecasted insights to inform demand, inventory, and operations.

You will collaborate with data engineers and business stakeholders to translate manufacturing and supply chain data into scalable predictive models, ensuring governance and production

Qualifications

• BS in Computer Science, Information Technology, Data Science, or equivalent
• Proven background deploying ML models in production environments
• Ownership of forecasting or predictive analytics models from feature engineering through deployment, monitoring, retraining, and continuous improvement

Responsibilities

• Design, build, deploy, and support production machine learning solutions that drive meaningful business decisions
• Own the complete machine learning lifecycle, including feature engineering, training, deployment, monitoring, retraining, and continuous improvement
• Develop forecasting models supporting demand planning, inventory optimization, financial planning, operations, and production
• Build and maintain production feature pipelines and datasets within Databricks
• Monitor prediction accuracy, model drift, operational performance, and overall model health
• Investigate production model issues and improve solutions as business conditions and data evolve
• Partner with business stakeholders to understand operational challenges before designing technical solutions
• Translate manufacturing, supply chain, commercial, and operational data into scalable predictive solutions
• Collaborate with data engineering teams to integrate trusted enterprise data into production machine learning workflows
• Establish best practices for model lifecycle management, testing, governance, documentation, and production support
• Ensure forecasting and machine learning solutions are built on trusted, governed enterprise data
• Contribute to the organization's long-term AI, automation, and intelligent application strategy
• Apply modern AI tools to improve engineering productivity while maintaining sound software engineering practices
• Communicate technical decisions, model assumptions, and business impact to both technical and non-technical stakeholders

Skills

Time Series Forecasting

Databricks

Python

Predictive Analytics

Model Serving

SQL

MLFlow

Model Performance Evaluation

Predictive Modeling

Data Governance

Lakehouse

Unity Catalog

Education

BS in Computer Science, Information Technology, Data Science, or equivalent

Tools

Databricks

Apache Spark

Python

SQL

MLflow

Job description

Our partner is a global manufacturer of conveyorized car wash equipment, supporting a complex operation across sales, inventory, production, finance, customer information, work orders, deliveries, and field operations.

The organization is transforming how it uses data by building a trusted enterprise platform that supports forecasting, automation, and practical AI-driven decision-making. They are hiring an AI/ML Engineer to turn governed business data into production machine learning solutions that help teams anticipate demand, optimize inventory, improve planning, and make better operational decisions.

This role begins where traditional data engineering ends. You'll design, deploy, monitor, and continuously improve forecasting and predictive models within a modern Databricks environment while owning the full production lifecycle from feature engineering through model performance and retraining.

This is an ideal opportunity for an engineer who combines machine learning expertise with strong software engineering practices, production ownership, and a clear understanding of how predictive solutions create measurable business value.

Experience and Education:

• BS in Computer Science, Information Technology, Data Science, or equivalent experience/field
• Proven background building, deploying, and supporting machine learning models used in production business environments
• Proven ownership of forecasting or predictive analytics models from feature engineering through deployment, monitoring, retraining, and continuous improvement
• Strong experience using Databricks, Apache Spark, Python, SQL, and MLflow within enterprise environments
• Experience building production feature pipelines and supporting modern data engineering practices
• Exposure to manufacturing, supply chain, logistics, retail, consumer products, or other operationally complex industries preferred
• Experience integrating machine learning solutions with enterprise data platforms, ERP systems, or business applications

Skills and Strengths:

• Time Series Forecasting
• Databricks
• Python
• Predictive Analytics
• Model Serving
• SQL
• MLFlow
• Model Performance Evaluation
• Predictive Modeling
• Data Governance
• Lakehouse
• Unity Catalog

Primary Job Responsibilities:

• Design, build, deploy, and support production machine learning solutions that drive meaningful business decisions
• Own the complete machine learning lifecycle, including feature engineering, training, deployment, monitoring, retraining, and continuous improvement
• Develop forecasting models supporting demand planning, inventory optimization, financial planning, operations, and production
• Build and maintain production feature pipelines and datasets within Databricks
• Monitor prediction accuracy, model drift, operational performance, and overall model health
• Investigate production model issues and improve solutions as business conditions and data evolve
• Partner with business stakeholders to understand operational challenges before designing technical solutions
• Translate manufacturing, supply chain, commercial, and operational data into scalable predictive solutions
• Collaborate with data engineering teams to integrate trusted enterprise data into production machine learning workflows
• Establish best practices for model lifecycle management, testing, governance, documentation, and production support
• Ensure forecasting and machine learning solutions are built on trusted, governed enterprise data
• Contribute to the organization's long-term AI, automation, and intelligent application strategy
• Apply modern AI tools to improve engineering productivity while maintaining sound software engineering practices
• Communicate technical decisions, model assumptions, and business impact to both technical and non-technical stakeholders