Machine Learning Engineer| AI - US

Role Overview

The Machine Learning / AI Engineer designs, builds, and operationalizes machine learning models and AI-powered solutions that create measurable business value for mSupply’s distribution operations (plumbing, appliance, and HVAC). The role bridges data science experimentation and production-grade engineering, ensuring models (predictive, recommendation, and generative/agentic AI) are deployed, monitored, and maintained reliably at scale.

Responsibilities

ML Model Development & Deployment

• Design, develop, and deploy production ML models and AI features (forecasting, classification, regression, clustering, recommendation).
• Build and maintain MLOps pipelines (data prep, feature engineering, training, evaluation, versioning, deployment, monitoring).
• Operationalize AI solutions tied to distribution outcomes: demand forecasting, inventory optimization, dynamic pricing, and churn/segmentation.
• Package and serve models via REST APIs, batch inference pipelines, and/or embedded integrations in the Microsoft Fabric/Azure ecosystem.
• Implement monitoring and alerting for drift, degradation, and data quality issues.

Generative AI & LLM Engineering

• Build applications using LLMs and generative AI, including RAG pipelines, semantic search, document intelligence, and AI-assisted workflows.
• Apply prompt engineering, fine-tuning, and evaluation for LLM-based features.
• Integrate Azure OpenAI Service and Azure AI services into data platform workflows and business applications.
• Apply responsible AI practices (bias evaluation, hallucination mitigation, explainability).

Feature Engineering & Data Platform Integration

• Collaborate with Data Engineers to create feature stores and curated feature pipelines in the medallion architecture (Bronze → Silver → Gold), including Gold-layer feature production.
• Write performant SQL and Python transformations using dbt and Microsoft Fabric.
• Ensure inputs are documented, tested, and aligned with upstream data contracts.

Cross-Functional Collaboration

• Partner with Data Scientists to convert research and experimentation into production systems.
• Work with Business Product Owners and stakeholders to define ML use cases, assess business impact, and prioritize work across business units.
• Participate in Agile ceremonies (e.g., sprint planning and backlog refinement).