Director, AI Platform and Development Engineering; Remote - Global

About WAI

Since 1978, WAI has grown from an entrepreneurial start‑up into a global aftermarket leader headquartered in South Florida. Nearly five decades of product knowledge, customer trust, and operational scale now support an ambitious growth agenda across distribution, manufacturing, product, customer, supply chain, and shared‑service operations.

Contract

The Director, AI Platform Engineer is a senior technical leadership role responsible for building and governing WAI's AI‑ready data foundation, model workflows, retrieval architecture, analytics intelligence layer, and production AI platform capabilities.

This role owns the technical platform that enables WAI's AI automation strategy, including the design and build of a centralized data lake that consolidates critical data from ERP and other core business systems, data ingestion, cleansing, normalization, unified schema design, machine learning workflows, LLM‑powered insights, dashboards, natural‑language querying, retrieval‑augmented generation (RAG), embeddings, vector search, model serving, monitoring, and technical governance. The role works closely with IT, infrastructure, business data owners, the AI Automation team, offshore engineers, vendors, and functional leaders to deliver trusted, secure, scalable, and cost‑effective AI capabilities grounded in WAI data.

• Lead the design, build, deployment, and continuous improvement of WAI's AI‑ready data and platform foundation across sales, inventory, planning, catalog, customer, order, product, and related business systems.
• Design, build, and govern a centralized data lake that consolidates critical data from ERP and other core business systems into a single trusted foundation, enabling AI tools, models, and analytics to reliably access enterprise data.
• Identify repetitive and manual tasks, use process mining to uncover workflow bottlenecks, and implement RPA solutions to improve efficiency and streamline operations.
• Own technical architecture for AI/ML/LLM workflows, RAG, embeddings, vector search, structured data query, dashboards, APIs, model serving, and monitoring.
• Connect, ingest, clean, validate, normalize, and automate data pipelines from structured and unstructured sources, including enterprise systems, reports, documents, PDFs, spreadsheets, and business notes.
• Build or oversee a trusted unified data layer with schema standards, data‑quality monitoring, lineage, source traceability, and failure detection.
• Develop and support machine learning and statistical methods for revenue trends, sales forecasting, anomaly detection, stock monitoring, shortage/overstock prediction, demand forecasting, variance analysis, and risk identification.
• Build grounded LLM workflows that connect to trusted WAI data, generate AI summaries, support natural‑language business questions, reduce hallucinations, and return business‑friendly explanations with source references.
• Implement embeddings and vector search capabilities using pgvector or other approved vector database technologies, tuned for retrieval precision, speed, broad scanning, and deep analysis.
• Build or support dashboards, KPIs, forecasts, anomaly alerts, AI summaries, drill‑down to source data, automatic refresh, and exportable leadership or business reports.
• Deploy reliable pipelines, models, APIs, dashboards, and LLM workflows while optimizing inference cost, latency, GPU memory, throughput, model selection, and production performance.
• Implement role‑based access control, auditability, data governance, source traceability, monitoring, evaluation, and verifiable AI outputs in partnership with IT/security stakeholders.
• Provide technical direction to offshore AI engineers, data/integration engineers, vendors, and implementation partners.
• Partner with the AI Automation Director and business‑facing teams to ensure platform work is aligned to approved use cases, business value, adoption needs, and governance priorities.
• Evaluate hosted AI services, open‑source models, AI/ML frameworks, orchestration tools, and proof‑of‑concepts; recommend when to use hosted models versus self‑hosted or WAI‑tuned models.

Undertake…