Machine Learning Engineer| AI - US

Remote Status: Remote Job Id:  3740-mSupply # of Openings:  1

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• 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.

• 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.

• 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

• 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