Brief Description

AI Engineer

Reports to: Director of Data Enablement | Location: Westlake, OH | Hybrid

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. The role focuses on building practical, scalable solutions for high-value business needs such as strategic pricing, margin leakage analysis, supplier price-change workflows, order remark classification, touchless order enablement, and executive KPI modernization.

This role partners closely with IT, which owns Databricks as an enterprise technology platform, including infrastructure, integrations, security architecture, CI/CD frameworks, and environment provisioning. The Data & AI Solutions Engineer contributes to solution design, analytical modeling, AI workflow development, evaluation, monitoring, and production readiness in alignment with those standards.

What This Role Owns

Advanced analytics and AI-enabled solution development within the governed lakehouse environment.
Predictive, machine learning, and AI-assisted workflows that support business decision-making.
Reusable patterns for moving data and AI use cases from prototype to governed production rollout.
Analytical models and feature engineering for priority use cases.
AI evaluation, monitoring, confidence scoring, and human-in-the-loop design.
Documentation of solution logic, assumptions, testing methods, and business impact.
Technical partnership with the Analytics Product Engineer to turn models and insights into usable analytics products.

Partnership Boundary

This role works closely with IT and App Development but does not own Databricks infrastructure architecture, core-system integrations, security architecture, identity/access management, enterprise CI/CD frameworks, environment provisioning, ERP/CRM systems of record, or source-system data quality enforcement at the pipeline level.

Key Responsibilities

Advanced Analytics and AI Solution Delivery

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.
Develop feature engineering approaches for use cases such as pricing intelligence, churn/customer health, product or customer segmentation, and order automation.
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.

Priority 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.

First 12-18 Month Success Outcomes

2-4 advanced analytics or AI-enabled solutions moved from concept to production-ready deployment.
Reusable patterns established for AI workflow evaluation, monitoring, and human-in-the-loop review.
Strategic pricing and/or margin leakage analytical capabilities materially advanced.
Supplier price-change analysis workflow improved through automation, exception detection, or decision support.
Order remark classification use case prototyped or deployed to support touchless order progress.
Trusted partnership established with IT, App Development, Data Analytics, and business stakeholders.

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.

Working Style

Practical builder who values business outcomes over technical novelty.
Comfortable working in shared ownership environments where IT, Data Enablement, and business teams each own part of the outcome.
Strong partner to IT, with respect for platform stability, security, deployment discipline, and change control.
Curious, analytical, and willing to challenge assumptions.
Able to move quickly without creating unnecessary risk.
Clear communicator who can explain what an AI or analytics solution does, how it was tested, and where human judgment is still required.

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