Machine Learning Engineer| AI - US

About mSupply™ mSupply is a North American distributor of OEM repair parts and equipment serving the appliance, HVAC and plumbing industries. Headquartered in St. Louis, the company combines industry expertise with a broad product selection and a national distribution network. With 2,000 employees across the United States and Canada, mSupply delivers speed and reliability at scale, with a vast product inventory and same-day shipping. Its family of brands is focused on making sure customers always get the Right Products. Right Now.™ For more information, visit mSupply.com. Job Summary The Machine Learning / AI Engineer is responsible for designing, building, and operationalizing machine learning models and AI-powered solutions that drive measurable business value across mSupply's plumbing, appliance, and HVAC distribution operations. This role bridges the gap between data science experimentation and production-grade engineering, ensuring that predictive models, recommendation systems, generative and agentic AI capabilities are reliably deployed, monitored, and maintained at scale. Working within mSupply's enterprise data platform — built on Microsoft Fabric, dbt, and Power BI — this engineer partners closely with Data Scientists, Data Engineers, and Business Product Owners to translate analytical models into reliable, scalable, and observable production systems. The role operates in a distribution-industry context where model outcomes directly influence inventory positioning, demand forecasting, pricing strategy, customer segmentation, and operational efficiency across a multi-location, multi-business-unit environment. Job Duties & Responsibilities ML Model Development & Deployment Design, develop, and deploy machine learning models and AI features into production environments, including forecasting, classification, regression, clustering, and recommendation systems. Build and maintain MLOps pipelines — covering data preparation, feature engineering, model training, evaluation, versioning, deployment, and monitoring — using industry-standard tooling. Develop and operationalize AI-powered solutions relevant to distribution operations, including demand forecasting, inventory optimization, dynamic pricing models, and customer churn or segmentation models. Package and serve models via REST APIs, batch inference pipelines, or embedded integrations within the Microsoft Fabric and Azure ecosystem. Implement model monitoring and alerting frameworks to detect drift, degradation, and data quality issues in production. Generative AI & LLM Engineering Design and build applications using large language models (LLMs) and generative AI, including retrieval-augmented generation (RAG) pipelines, semantic search, document intelligence, and AI-assisted workflows. Implement prompt engineering, fine-tuning, and evaluation frameworks for LLM-based features. Integrate Azure OpenAI Service and related Azure AI services into data platform workflows and business-facing applications. Apply responsible AI practices including bias evaluation, hallucination mitigation, and explainability techniques. Feature Engineering & Data Platform Integration Collaborate with Data Engineers to design and build feature stores and curated feature pipelines in the Gold layer of the medallion architecture (Bronze → Silver → Gold). Write performant SQL and Python transformations within dbt and Microsoft Fabric to produce high-quality, reusable feature sets. Ensure model inputs are well-documented, tested, and aligned with upstream data contracts. Cross-Functional Collaboration Partner with Data Scientists on model research and experimentation, translating proof-of-concept work into production-ready engineering. Work with Business Product Owners and functional stakeholders across HVAC, plumbing, and appliance business units to define ML use cases, evaluate business impact, and prioritize model development. Participate in Agile ceremonies including sprint planning, backlog refinement, and sprint reviews. Communicate model performance, limitations, and trade-offs clearly to both technical and non-technical audiences. MLOps & Engineering Excellence Establish and maintain MLOps best practices including CI/CD for model pipelines, automated testing, experiment tracking, model registry management, and reproducible training workflows. Leverage tools such as MLflow, Azure Machine Learning, or Fabric's native ML capabilities for lifecycle management. Champion code quality, documentation, and reusable engineering patterns within the ML/AI stack. Contribute to the team's internal standards for model governance, documentation, and risk classification. Qualifications Required Bachelor's degree in Computer Science, Software Engineering, Mathematics, Statistics, Data Science, or a related quantitative field. 3+ years of experience in machine learning engineering, MLOps, or applied AI engineering roles with production deployments. Strong proficiency in Python, including 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 understanding of 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. Familiarity with REST API development and model serving patterns. Preferred Experience with Microsoft Fabric, Azure Synapse Analytics, or Azure OpenAI Service. Hands-on experience with LLMs, RAG architectures, vector databases (e.g., Azure AI Search, Pinecone, pgvector), and prompt engineering. Experience with dbt or similar transformation frameworks in a medallion architecture. Background in wholesale distribution, supply chain, HVAC, plumbing, or related industrial B2B sectors. Familiarity with forecasting frameworks such as Prophet, NeuralProphet, or Nixtla. Experience with feature stores (Feast, Tecton, or similar) and data-centric AI practices. Knowledge of responsible AI, model explainability (SHAP, LIME), and fairness evaluation. Familiarity with Agile delivery methodologies and working within a product-oriented team model. 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 careers@msupply.com. #mSupply 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. 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