Engineer, Machine Learning AI
mSupply is North America’s leading distributor of OEM repair parts and equipment, serving professionals in the appliance, HVAC, plumbing, commercial kitchen and pool/spa industries. Headquartered in St. Louis, MO, mSupply is a multi-billion dollar enterprise offering an extensive product range, industry expertise and seamless service. With more than 2,000 associates across the U.S. and Canada, mSupply’s family of brands delivers with speed, reliability and precision through its branches, distribution centers and extensive fleet of delivery vehicles. Shipped orders reach 93% of U.S. customers via next-day ground delivery and 100% within two days. For more information, visit mSupply.com.
The ML/AI Engineer designs, builds, and operationalizes machine learning models and AI-powered solutions that drive measurable business value across mSupply’s distribution operations. Working within the enterprise data platform (Microsoft Fabric, dbt, Power BI), this role partners with Data Scientists, Data Engineers, and Business Product Owners to turn analytical models into reliable, scalable production systems.
You will bridge the gap between data science experimentation and production-grade AI. Your models will directly influence inventory positioning, demand forecasting, pricing strategy, and customer segmentation across a multi-location distribution network. If you want to build ML systems that run at scale in a real operational environment, not just prototypes that never ship, this is the role.
Job Duties & Responsibilities
Design, develop, and deploy machine learning models into production, including forecasting, classification, clustering, recommendation, and optimization systems.
Build and maintain MLOps pipelines covering data preparation, feature engineering, model training, evaluation, versioning, deployment, and monitoring.
Design and build applications using LLMs and generative AI, including RAG pipelines, semantic search, document intelligence, and AI-assisted workflows.
Implement prompt engineering, fine-tuning, and evaluation frameworks for LLM-based features, integrating Azure OpenAI Service into platform workflows.
Collaborate with Data Engineers to design feature stores and curated feature pipelines in the Gold layer of the medallion architecture.
Partner with Data Scientists to translate proof-of-concept models into production-ready engineering, and with Business Product Owners to define and prioritize ML use cases.
Implement model monitoring and alerting frameworks to detect drift, degradation, and data quality issues in production.
Package and serve models via REST APIs, batch inference pipelines, or embedded integrations within the Fabric and Azure ecosystem.
Establish MLOps best practices including CI/CD for model pipelines, experiment tracking, model registry management, and reproducible training workflows.
Qualifications
Required
Bachelor’s degree in Computer Science, Software Engineering, Mathematics, Statistics, Data Science, or a related quantitative field.
3+ years of experience in ML engineering, MLOps, or applied AI engineering with production deployments.
Strong proficiency in Python and ML libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
Hands-on experience building and maintaining MLOps pipelines, including experiment tracking, model versioning, and CI/CD for ML workloads.
Solid software engineering fundamentals: version control (Git), testing, code reviews, and modular design.
Experience with cloud-based ML platforms (Azure Machine Learning, AWS SageMaker, or equivalent).
Proficiency in SQL and experience working with large-scale structured datasets.
Preferred
Experience with Microsoft Fabric, Azure Synapse Analytics, or Azure OpenAI Service.
Hands-on experience with LLMs, RAG architectures, vector databases, and prompt engineering.
Experience with dbt or similar transformation frameworks in a medallion architecture.
Background in wholesale distribution, supply chain, HVAC, plumbing, or related B2B sectors.
Familiarity with forecasting frameworks such as Prophet, NeuralProphet, or Nixtla.
Experience or demonstrated ability using AI-powered tools (e.g., code assistants, LLMs, AI-augmented workflows) to accelerate work and improve outcomes.
Physical Demands & Work Environment
This position may require over 40 hours per week and includes regular physical activity such as:
Primarily sedentary work, requiring extended periods of sitting while working at a computer
Ability to communicate effectively in person, via phone, and through virtual collaboration tools
Occasional standing, walking, and movement during meetings, site visits, or travel
Ability to lift and carry light materials (up to 15 lbs) occasionally, such as a laptop or presentation materials
Ability to travel periodically for business needs, including visiting distribution centers, suppliers, stores, or industry events
Office environment includes standard business equipment such as computers, phones, and conferencing tools
What We Offer:
We prioritize your well-being from day one with a comprehensive benefits package that includes:
Medical, dental, vision, and prescription coverage effective immediately
401(k) plan with company contributions
Life insurance and short-term disability coverage
HSA/FSA options and an Employee Assistance Program (EAP)
Paid time off, including vacation, holidays, and personal days
Weekly pay, employee discounts, and more
Equal Employment Opportunity & Pre-Employment Requirements
mSupply is an Equal Opportunity Employer. We make employment decisions without regard to sex, age, race, color, creed, religion, national origin, citizenship or immigration status, sexual orientation, gender identity or expression, disability, genetic information, marital status, veteran or military status, or any other status protected by applicable federal, state, or local law.
We are committed to providing reasonable accommodations for qualified individuals with disabilities and to applicants with sincerely held religious beliefs, in accordance with applicable law. To request a reasonable accommodation, please contact [email protected].
Final offers of employment may be contingent upon completion of job-related pre-employment checks and screenings permitted by law for the position. For roles that require operation of a company vehicle, a Motor Vehicle Record (MVR) check may also be conducted to determine insurability. This employer participates in E-Verify to confirm employment eligibility in the United States.
The ML/AI Engineer designs, builds, and operationalizes machine learning models and AI-powered solutions that drive measurable business value across mSupply’s distribution operations. Working within the enterprise data platform (Microsoft Fabric, dbt, Power BI), this role partners with Data Scientists, Data Engineers, and Business Product Owners to turn analytical models into reliable, scalable production systems.
You will bridge the gap between data science experimentation and production-grade AI. Your models will directly influence inventory positioning, demand forecasting, pricing strategy, and customer segmentation across a multi-location distribution network. If you want to build ML systems that run at scale in a real operational environment, not just prototypes that never ship, this is the role.
Job Duties & Responsibilities
Design, develop, and deploy machine learning models into production, including forecasting, classification, clustering, recommendation, and optimization systems.
Build and maintain MLOps pipelines covering data preparation, feature engineering, model training, evaluation, versioning, deployment, and monitoring.
Design and build applications using LLMs and generative AI, including RAG pipelines, semantic search, document intelligence, and AI-assisted workflows.
Implement prompt engineering, fine-tuning, and evaluation frameworks for LLM-based features, integrating Azure OpenAI Service into platform workflows.
Collaborate with Data Engineers to design feature stores and curated feature pipelines in the Gold layer of the medallion architecture.
Partner with Data Scientists to translate proof-of-concept models into production-ready engineering, and with Business Product Owners to define and prioritize ML use cases.
Implement model monitoring and alerting frameworks to detect drift, degradation, and data quality issues in production.
Package and serve models via REST APIs, batch inference pipelines, or embedded integrations within the Fabric and Azure ecosystem.
Establish MLOps best practices including CI/CD for model pipelines, experiment tracking, model registry management, and reproducible training workflows.
Qualifications
Required
Bachelor’s degree in Computer Science, Software Engineering, Mathematics, Statistics, Data Science, or a related quantitative field.
3+ years of experience in ML engineering, MLOps, or applied AI engineering with production deployments.
Strong proficiency in Python and ML libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
Hands-on experience building and maintaining MLOps pipelines, including experiment tracking, model versioning, and CI/CD for ML workloads.
Solid software engineering fundamentals: version control (Git), testing, code reviews, and modular design.
Experience with cloud-based ML platforms (Azure Machine Learning, AWS SageMaker, or equivalent).
Proficiency in SQL and experience working with large-scale structured datasets.
Preferred
Experience with Microsoft Fabric, Azure Synapse Analytics, or Azure OpenAI Service.
Hands-on experience with LLMs, RAG architectures, vector databases, and prompt engineering.
Experience with dbt or similar transformation frameworks in a medallion architecture.
Background in wholesale distribution, supply chain, HVAC, plumbing, or related B2B sectors.
Familiarity with forecasting frameworks such as Prophet, NeuralProphet, or Nixtla.
Experience or demonstrated ability using AI-powered tools (e.g., code assistants, LLMs, AI-augmented workflows) to accelerate work and improve outcomes.
Physical Demands & Work Environment
This position may require over 40 hours per week and includes regular physical activity such as:
Primarily sedentary work, requiring extended periods of sitting while working at a computer
Ability to communicate effectively in person, via phone, and through virtual collaboration tools
Occasional standing, walking, and movement during meetings, site visits, or travel
Ability to lift and carry light materials (up to 15 lbs) occasionally, such as a laptop or presentation materials
Ability to travel periodically for business needs, including visiting distribution centers, suppliers, stores, or industry events
Office environment includes standard business equipment such as computers, phones, and conferencing tools
What We Offer:
We prioritize your well-being from day one with a comprehensive benefits package that includes:
Medical, dental, vision, and prescription coverage effective immediately
401(k) plan with company contributions
Life insurance and short-term disability coverage
HSA/FSA options and an Employee Assistance Program (EAP)
Paid time off, including vacation, holidays, and personal days
Weekly pay, employee discounts, and more
Equal Employment Opportunity & Pre-Employment Requirements
mSupply is an Equal Opportunity Employer. We make employment decisions without regard to sex, age, race, color, creed, religion, national origin, citizenship or immigration status, sexual orientation, gender identity or expression, disability, genetic information, marital status, veteran or military status, or any other status protected by applicable federal, state, or local law.
We are committed to providing reasonable accommodations for qualified individuals with disabilities and to applicants with sincerely held religious beliefs, in accordance with applicable law. To request a reasonable accommodation, please contact [email protected].
Final offers of employment may be contingent upon completion of job-related pre-employment checks and screenings permitted by law for the position. For roles that require operation of a company vehicle, a Motor Vehicle Record (MVR) check may also be conducted to determine insurability. This employer participates in E-Verify to confirm employment eligibility in the United States.