Senior AI Architect

JB Pointdexter•Houston, TX

16h

About The Position

J.B. Poindexter & Co., Inc. (JBPCO) is a privately held, diversified manufacturing company forecasting over $2.5 billion in annual revenues and employing over 8,000 team members. Its six operating subsidiaries span 60 locations and produce commercial truck bodies, step-vans, utility trucks, ambulances, electric and alternative-fuel vehicles, and expandable foam plastic packaging. The Senior AI Architect designs and implements enterprise AI solutions that improve business processes, reduce manual work, strengthen decision-making, and create measurable business value. Working within Information Technology and closely with the Office of AI, business leaders, technical teams, and vendors, the role translates AI strategy, platform direction, and governance standards established by the Senior Director of AI into scalable technical designs and production-ready solutions. The position combines architecture leadership with hands-on technical work, including solution design, prototyping, coding, technical evaluation, and implementation partnership. Initial priorities focus on manufacturing, supply chain, operations, sales, and customer-facing processes, followed by corporate functions. The role reports directly to the Chief Information Officer and provides technical direction and coaching without direct supervisory responsibility.

Requirements

Bachelor's degree in computer science, engineering, data science, information systems, or a related field.
At least five years of relevant experience designing and delivering artificial intelligence, machine learning, advanced analytics, or related enterprise technology solutions.
Experience designing enterprise AI architectures and translating business requirements into technical solutions.
Knowledge of responsible AI, data privacy, cybersecurity, model risk, and regulatory considerations.
Hands-on experience developing prototypes and production solutions using Python and SQL.
Experience with generative AI, retrieval-augmented generation, predictive analytics, and AI assistants or multi-agent systems.
Experience working with cloud platforms such as Microsoft Azure or Amazon Web Services.
Experience with enterprise data and analytics platforms such as Databricks or Microsoft Fabric.
Knowledge of enterprise integration, application programming interfaces, data pipelines, identity and access management, and cloud architecture.
Experience with practices for deploying, monitoring, maintaining, and improving AI and machine-learning solutions.
Ability to evaluate technical options and clearly explain architecture decisions, business implications, risks, and tradeoffs.
Experience working across internal teams, shared services, vendors, and other delivery partners.
Demonstrated ability to manage multiple technical priorities in a complex enterprise environment.

Nice To Haves

Master's degree in computer science, engineering, artificial intelligence, data science, or a related field.
Experience applying AI in manufacturing, supply chain, operations, sales, or customer service environments.

Responsibilities

Design scalable enterprise AI architectures aligned with business priorities, enterprise strategy, approved platforms, and technical standards.
Translate business requirements into solution designs, technical specifications, integration requirements, and implementation plans.
Evaluate architecture options and recommend solutions based on business value, feasibility, security, scalability, cost, and long-term support needs.
Provide architecture guidance and technical reviews throughout the solution lifecycle.
Build prototypes and proofs of concept to test AI use cases, technical assumptions, and potential business value, and move successful pilots into secure, reliable, scalable production solutions.
Write and review code for AI solutions, integrations, automation workflows, and data-processing components.
Apply generative AI, predictive analytics, retrieval-augmented generation, and multi-agent approaches where appropriate.
Document prototypes, technical decisions, reusable components, and lessons learned; establish testing, evaluation, monitoring, logging, auditability, and performance requirements.
Design AI solutions for manufacturing, supply planning, material availability, supplier performance, quality, and operational decision-making.
Develop predictive and intelligence capabilities to identify shortages, production risks, process issues, and planning constraints.
Partner with operational leaders and technical teams to embed AI into existing workflows and systems, with appropriate validation, user review, and human approval for important operational decisions.
Design AI-enabled solutions to improve sales processes, customer service, response speed, information access, and decision support.
Identify opportunities to automate repetitive work, improve workflow accuracy, and provide employees with timely, relevant information.
Partner with commercial and customer-facing teams to translate business needs into practical, measurable AI use cases and promote adoption through usable, reliable solutions integrated into established processes.
Design integrations between AI solutions and enterprise applications, data platforms, business systems, and workflow tools.
Promote reusable architecture patterns, shared services, common components, and approved development approaches; work with IT teams on infrastructure, identity, access, data, security, and support requirements.
Provide technical direction and coaching to developers, vendors, and implementation partners without direct supervisory responsibility.
Partner with the Senior Director of AI and the Office of AI to align solutions with enterprise strategy, governance, and platform decisions.
Collaborate with business owners, data teams, cybersecurity, application teams, and infrastructure teams throughout delivery; communicate technical decisions, tradeoffs, dependencies, and risks in clear business language.
Work with business owners to define success measures before development and evaluate outcomes such as cycle-time reduction, employee effort saved, accuracy, adoption, risk reduction, and financial impact.
Recommend whether solutions should be expanded, redesigned, reused, or discontinued based on results.
Monitor AI technology developments and recommend practical applications aligned with enterprise priorities.