AI/ML Data Engineer

Role Description

The AI/ML Engineer builds, deploys, and supports production forecasting and AI capabilities on top of the company’s governed Databricks lakehouse foundation. This role focuses on:

• Feature engineering
• Training and inference workflows
• Model evaluation
• Model serving
• Monitoring
• AI-ready outputs that support forecasting, business automation, intelligent recommendations, executive reporting, and AI/BI consumption

This role is accountable for turning trusted enterprise data into production AI and forecasting capabilities that are reliable, measurable, governed, and usable by the business.

Responsibilities include:

• Build and maintain feature pipelines, training datasets, and forecast workflows for various use cases including revenue, demand, delivery timing, customer behavior, inventory risk, process performance, and operational planning.
• Operationalize forecasting and machine learning models through repeatable training, evaluation, deployment, inference, and monitoring patterns in Databricks.
• Deploy and support batch, near-real-time, and API-based inference outputs for dashboards, Databricks Apps, workflow automation, business alerts, and decision-support tools.
• Implement model performance tracking, drift monitoring, validation checks, error handling, and traceability from source data through feature logic to prediction output.
• Partner with Data Engineering and BI teams to ensure forecast outputs, KPIs, business logic, and AI-enabled metrics align to governed semantic structures and reporting standards.
• Create reusable notebooks, libraries, feature engineering patterns, evaluation templates, and deployment frameworks that accelerate enterprise AI adoption while remaining supportable.
• Support AI/BI and agent-based consumption by preparing structured, governed, business-readable outputs that can be used by reporting tools, applications, and AI assistants.
• Translate forecasting and AI outputs into measurable operational or financial impact, including revenue opportunity, margin improvement, demand planning, service performance, inventory optimization, and process automation.

Qualifications

• 5+ years of experience in machine learning engineering, data engineering, analytics engineering, applied AI engineering, or production forecasting.
• Strong hands-on experience with Databricks, Spark, SQL, Python, and production-grade data pipeline development.
• Experience building forecasting or machine learning solutions in production, including feature preparation, model training, evaluation, deployment, monitoring, and support.
• Experience with model lifecycle practices, versioning, validation, performance tracking, and production release processes.
• Ability to connect technical AI and forecasting work to measurable financial, operational, or customer-facing outcomes.
• Strong understanding of data quality, metric consistency, semantic validation, and governed enterprise reporting needs.

Requirements

• Experience with MLflow, Mosaic AI Model Serving, Unity Catalog-governed AI assets, Databricks Workflows, Feature Engineering, and model monitoring patterns.
• Experience supporting forecasting, automation, or recommendation use cases in manufacturing, distribution, supply chain, finance, sales, or service operations.
• Experience preparing AI-ready outputs for dashboards, applications, workflow automation, or conversational AI/BI use cases.
• Exposure to vector search, retrieval-augmented generation, agentic workflows, or AI assistant patterns is a plus.
• Experience with time-series forecasting, demand planning, revenue forecasting, inventory optimization, or operational prediction models is strongly preferred.

Benefits

• 100% employer paid medical plan
• 401(k) match
• Additional medical plans
• Dental and vision coverage
• Flex spending account
• Short-term and long-term disability & life insurance coverage