ML Engineer (AI-Native Systems & Forecasting)

about 1 month ago

Responsibilities

Design, build, and deploy production-grade ML systems for demand forecasting and labor optimization.
Own the full ML lifecycle, including data ingestion, feature engineering, model training, deployment, and monitoring.
Inherit and remediate messy, inconsistent datasets and establish scalable data pipelines.
Architect data systems across ingestion, warehousing, transformation, and feature stores.
Build and maintain LLM-native systems, including RAG pipelines, prompt systems, and evaluation frameworks.
Make pragmatic decisions on modeling approaches, including when to use APIs, fine-tuning, or custom models.
Design and implement model evaluation systems that measure performance continuously.
Implement monitoring, drift detection, and feedback loops to improve model performance over time.
Design and run experiments, including A/B testing and statistical validation of model performance.
Translate model performance and tradeoffs into clear insights for product and business stakeholders.
Collaborate closely with Product, Engineering, and Operations to integrate ML into core workflows.

5–10+ years of experience in machine learning, data science, or applied AI roles.
Proven experience shipping ML systems into production environments.
Strong experience working with real-world, imperfect datasets in mid-maturity or scaling organizations.
Deep understanding of the full data stack, including ingestion, warehousing, feature engineering, and model serving.
Experience designing and operating ML pipelines and workflows in production.
Hands-on experience with LLM systems, including RAG, prompt design, and evaluation frameworks.
Strong foundation in statistics, experimentation, and model evaluation.
Experience with monitoring, observability, and model performance tracking over time.
Ability to operate with high ownership, ambiguity, and minimal process overhead.
Strong communication skills, with the ability to translate technical decisions into business impact.

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