AI Engineer

Role Summary

The Data & AI Solutions Engineer designs, builds, and supports advanced analytics and AI-enabled solutions that turn Palmer Holland's governed lakehouse data into measurable business value. Reporting to the Director of Data Enablement, this role works at the intersection of data engineering, advanced analytics, applied AI, and business problem-solving.

Key Responsibilities

• Design and build advanced analytics solutions that support high-priority business problems.
• Develop analytical models for strategic pricing, margin leakage, price realization, customer behavior, supplier price changes, and other commercial and operational use cases.
• Build repeatable solution patterns that can be reused across domains rather than one-off analytical work.
• Operational workflow automation: agentic and AI-assisted solutions that reduce manual back-office effort across order management, procurement, and logistics operations — including intelligent document processing, exception-based triage, and touchless workflow enablement.
• Translate business needs into technical solution designs in partnership with the Director of Data Enablement, IT, and business stakeholders.
• Applied AI and Machine Learning: Build and support predictive models and AI-assisted workflows using governed data from the lakehouse.
• Create evaluation methods, golden datasets, test cases, and monitoring processes for AI-enabled solutions.
• Design human-in-the-loop processes where AI outputs require review, approval, or exception handling.
• Partner with IT on production deployment standards, logging, monitoring, operational readiness, token optimization, and cost control.
• Lakehouse-Enabled Business Solutions: Build solutions that use curated, trusted, and certified datasets to solve practical business problems.
• Contribute to silver-layer modeling and transformation work in partnership with IT, where advanced analytics or AI solutions require conformed, business-ready data structures not yet available in the governed layer.
• Support development of gold-layer analytical outputs in partnership with the Director of Data Enablement and domain stakeholders.
• Contribute to semantic modeling and certified dataset creation where advanced analytics or AI use cases require reusable business logic.
• Partner with IT and App Development when analytical or AI solutions depend on data from ERP, CRM, Esker, O365, or other enterprise systems.
• Prioritize Use Case Support: Strategic pricing and margin leakage: models and workflows that surface margin risk, price realization gaps, and pricing decision opportunities.
• Fully-loaded profitability analysis: scalable frameworks that trace sales through supply chain history to surface true pocket profitability across freight, warehousing, financing, and disposal costs — building on existing analytical work toward a governed, continuously updated model.
• FPL / supplier price-change analysis: tools that compare supplier cost changes, identify material exceptions, and support pricing review workflows.
• Order remark classification and touchless order enablement: AI-assisted classification of order notes, remarks, and exception patterns to support Esker automation progress.
• Executive KPI and certified dashboard modernization: analytical models and governed datasets that support trusted executive reporting.
• Governance, Testing, and Production Readiness: Ensure AI and advanced analytics solutions are traceable, explainable, testable, and aligned with Palmer Holland governance standards.
• Document model inputs, assumptions, limitations, evaluation methods, taxonomy, and business rules.
• Partner with IT on release management, access controls, monitoring, and production deployment requirements.
• Support responsible AI practices, including appropriate use of sensitive data, confidence thresholds, fallback logic, and auditability.

Required Qualifications

• 5+ years of experience in data engineering, analytics engineering, applied AI, machine learning, data science, or advanced analytics solution delivery.
• Strong SQL and Python skills.
• Experience building analytical models or data products using enterprise data platforms.
• Practical experience with AI, ML, or GenAI-enabled applications, such as classification, prediction, recommendation, retrieval, evaluation, or workflow automation.
• Experience with modern lakehouse or cloud data platforms such as Databricks (preferred), Snowflake, Azure, AWS, or similar environments.
• Ability to translate ambiguous business problems into scalable analytical or AI-enabled solutions.
• Strong documentation habits and ability to explain technical concepts to business stakeholders.

Preferred Qualifications

• Certification in or experience with Databricks, Delta Lake, Unity Catalog, MLflow, vector search, model serving, or similar technologies.
• Experience with LLMOps, MLOps, AI evaluation frameworks, model monitoring, prompt/version control, or human-in-the-loop workflows.
• Experience in B2B distribution, specialty chemicals, ingredients, manufacturing, or regulated business environments.
• Experience supporting pricing, order-to-cash, customer analytics, sales enablement, supply chain, or finance use cases.
• Familiarity with data governance, semantic layers, certified datasets, and role-based access controls.