Sonny's Enterprises LLC

AI/ML Data Engineer

Vaga remota de AI ML Engineering com fit claro de localização do candidato.

Publicada24 de jul. de 2026

Países elegíveis1 país aceito

Sinal de senioridadeMiddle

Modelo de trabalhoRemoto

Locais aceitos para candidatos

Estados Unidos

Resumo da vaga

Conteúdo da vaga extraído em seções para revisão mais rápida.

Build and maintain feature pipelines, training datasets, and forecast workflows for revenue, demand, delivery timing, customer behavior, inventory risk, process performance, and operational planning use cases.• 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.

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.

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