Director, AI Platform and Development Engineering (Remote - Global)

WAI Global; Town of Florida (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

WAI Global in the United States is seeking a Director, AI Platform Engineer to build and govern an AI‑ready data foundation, model workflows, retrieval architecture, and production AI platform capabilities.

You will own centralized data lake design, ingestion, cleansing, and unified schema; enable AI tools, embeddings, RAG, dashboards, and secure governance while coordinating with IT, vendors, and business leaders.

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Software Engineering, Data Science, ML/AI required.
• Master’s degree preferred; equivalent senior technical experience may be considered.

Responsibilities

• Lead design, build, deployment and continuous improvement of AI-ready data platform.
• Design, build and govern centralized data lake from ERP and core systems.
• Identify bottlenecks and implement RPA to improve efficiency.
• Own architecture for AI/ML/LLM workflows, RAG, embeddings and dashboards.
• Ingest, clean, normalize, and automate data pipelines from structured/unstructured sources.
• Build unified data layer with schema standards, data quality, lineage and traceability.
• Develop ML methods for revenue forecasts, anomaly detection, stock monitoring, demand forecasting, risk id.
• Build LLM workflows that connect to trusted data and provide source references.
• Implement embeddings and vector search with pgvector or similar tools.
• Create dashboards, KPIs, forecasts, alerts, and exportable leadership reports.
• Deploy pipelines, models, APIs, dashboards with cost/latency optimization.
• Implement RBAC, governance, auditing and verifiable AI outputs with IT/security.

Skills

Python

SQL

APIs

JSON

ETL/ELT

Orchestration

Data lake architecture

Cloud deployment

Education

Bachelor's degree in CS/Info Systems/Data Eng

Master’s degree preferred

Tools

pgvector

LangChain

OpenAI APIs

Job description

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.

Description

That scale creates a meaningful opportunity for practical enterprise AI. WAI is investing in AI, automation, data, and digital capabilities that can improve how work gets done: reducing manual effort, increasing speed and quality, strengthening decision‑making, and helping teams serve customers more effectively.

About The Role

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.

What You’ll Do

• 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.

Requirements

Undertakes additional responsibilities and tasks as directed by management.

What We’re Looking For

Education: Bachelor's degree in Computer Science, Information Systems, Data Engineering, Software Engineering, Data Science, Machine Learning, Artificial Intelligence, or a related technical field required. Master’s degree preferred; equivalent senior technical experience may be considered.

Experience

• 10+ years of experience in software engineering, data engineering, AI/ML engineering, enterprise architecture, analytics engineering, cloud engineering, or related technical roles.
• Experience designing or leading production data platforms, data lakes or lakehouses, analytics platforms, AI/ML platforms, LLM/RAG solutions, model workflows, APIs, or enterprise integration architectures.
• Hands‑on experience with data pipelines, SQL, Python, APIs, cloud platforms, data modeling, orchestration, monitoring, and production support practices.
• Hands‑on experience with LLMs, RAG, embeddings, vector databases, prompt/evaluation workflows, AI agents, model serving, and AI orchestration frameworks required.
• Experience with ERP, CRM, catalog, inventory, planning, customer, order, or product data in an operationally complex business preferred.
• Experience directly managing or providing technical direction to offshore/remote engineering teams required, as this role’s direct reports will be based offshore; experience directing contractors, vendors, or implementation partners also preferred.

Core Competencies & Leadership Attributes

• Technical leadership with the ability to translate business strategy into scalable, secure, and maintainable platform architecture.
• Sound judgment regarding data quality, security, privacy, model reliability, human review, monitoring, and production readiness.
• Ability to balance speed of delivery with enterprise standards, governance, cost, and long‑term maintainability.
• Strong partnership skills with IT, business leaders, data owners, automation teams, security stakeholders, and offshore delivery resources.
• Analytical thinking and structured problem‑solving across data, systems, model, and workflow domains.
• Ownership mindset, attention to detail, curiosity, and continuous learning in a fast‑changing AI environment.
• Ability to evaluate emerging technologies pragmatically and select tools based on business value, risk, cost, and scalability.

Skills

• Advanced knowledge of Python, SQL, APIs, JSON, data pipelines, ETL/ELT, orchestration, data lake/lakehouse architecture, data modeling, and cloud deployment practices.
• Experience with cloud platforms such as Azure, AWS, or GCP; experience with model serving, GPUs, containerization, MLOps, observability, or cost optimization preferred.
• Working knowledge of LLMs, RAG, embeddings, vector databases such as pgvector or similar tools, prompt design, evaluation, and AI agents.
• Familiarity with LangChain, LangGraph, OpenAI APIs, Azure AI, Microsoft Copilot, open-source models such as Llama, Mistral, Qwen, or similar tools preferred.
• Ability to design secure, monitored, and governed AI workflows with role‑based access, audit logs, data quality checks, source traceability, and production support requirements.
• Ability to build dashboards, natural‑language querying solutions, AI summaries, exportable reports, and analytics products that connect to trusted data sources.
• Strong documentation, technical communication, vendor evaluation, and stakeholder management skills.
• English fluency required; additional languages are a plus based on business needs.

Licensing Or Other Special Certifications

No specific license required. Certifications in cloud platforms, data engineering, AI/ML, cybersecurity, enterprise architecture, Microsoft Azure AI, MLOps, or related technical areas are preferred.