AI/ML Data Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and sustain feature pipelines, training datasets, and forecasting workflows for revenue, demand, delivery timing, customer behavior, inventory risk, process performance, and operational planning applications.

• Operationalize forecasting and machine learning models through consistent training, evaluation, deployment, inference, and monitoring methodologies in Databricks.

• Implement and support batch, near-real-time, and API-based inference outputs for dashboards, Databricks Apps, workflow automation, business alerts, and decision-support systems.

• Establish model performance tracking, drift monitoring, validation checks, error handling, and traceability from the source data through feature logic to prediction outcomes.

• Collaborate with Data Engineering and BI teams to ensure forecast outputs, KPIs, business logic, and AI-driven metrics are aligned with governed semantic frameworks and reporting standards.

• Develop reusable notebooks, libraries, feature engineering patterns, evaluation templates, and deployment frameworks that expedite enterprise AI adoption while remaining maintainable.

• Facilitate AI/BI and agent-based consumption by generating structured, governed, business-readable outputs suitable for reporting tools, applications, and AI assistants.

• Convert forecasting and AI outcomes into quantifiable operational or financial impacts, including revenue opportunities, margin enhancements, demand planning, service performance, inventory optimization, and process automation.

⛳️ Requirements

• Over 5 years of experience in machine learning engineering, data engineering, analytics engineering, applied AI engineering, or production forecasting.

• Proficient hands-on experience with Databricks, Spark, SQL, Python, and the development of production-grade data pipelines.

• Experience in building forecasting or machine learning solutions for production, including feature preparation, model training, evaluation, deployment, monitoring, and support.

• Familiarity with model lifecycle practices, including versioning, validation, performance tracking, and production release processes.

• Capability to link technical AI and forecasting efforts to tangible financial, operational, or customer-facing results.

• Strong grasp of data quality, metric consistency, semantic validation, and the requirements for governed enterprise reporting.

🏝️ Benefits

• 100% employer-paid medical plan

• 401(k) match

• Additional medical plans

• Dental

• Vision

• Flexible spending account

• Short-term and long-term disability & life insurance coverage