Senior AI Architect

Houston, TX, US Senior AI Architect

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Skills & Technologies

AzurePython

About This Role

AI job market dashboard showing open roles by category

Company & Position Overview:

J.B. Poindexter & Co., Inc. (JBPCO) is a privately held, diversified manufacturing company forecasting over $2.5B in annual revenues and employing over 8,000 team members. The six operating subsidiaries, covering 60 locations, are engaged in the production of commercial truck bodies, step-vans, utility trucks, ambulances, electric and alternative fuel vehicles, and expandable foam plastic packaging. For more information, visit https://www.jbpoindexter.com.

The Senior AI Architect designs and implements the delivery of enterprise AI solutions that improve business processes, reduce manual work, strengthen decision-making, and create measurable business value.

This role works within Information Technology and closely with the Office of AI, business leaders, technical teams, and vendors, The Senior AI Architect translates the AI strategy, platform direction, and governance standards established by the Senior Director of AI into scalable technical designs and production-ready solutions.

The position balances architecture leadership with hands-on technical work. The individual in this role designs solutions develops prototypes, writes code, evaluates technical approaches, and partners on implementations. Initial priorities will focus on manufacturing, supply chain, operations, sales, and customer-facing processes, followed by corporate functions. This role reports directly to the Chief Information Officer.

Essential Duties and Responsibilities:

AI Solution Architecture

  • Design scalable AI architectures that align with the enterprise AI strategy, approved platforms, and technical standards.
  • Designs enterprise AI solutions aligned with business priorities and the direction established by the Office of AI.
  • Translates business requirements into solution designs, technical specifications, integration requirements, and implementation plans.
  • Evaluates architecture options and recommends solutions based on business value, feasibility, security, scalability, cost, and long-term support needs.
  • Provides architecture guidance and technical reviews throughout the solution lifecycle.

Prototyping and Technical Development

  • Builds prototypes and proofs of concept to test AI use cases, technical assumptions, and potential business value. Moves prototypes and pilots into secure, reliable, and scalable production solutions.
  • Writes and reviews code for selected AI solutions, integrations, automation workflows, and data-processing components.
  • Applies generative AI, predictive analytics, retrieval-augmented generation, and multi-agent approaches where appropriate for the business needs.
  • Documents prototypes, technical decisions, reusable components, and lessons learned.
  • Establishes appropriate testing, evaluation, monitoring, logging, auditability, and performance requirements.

Manufacturing, Supply Chain, and Operational Solutions

  • Designs AI solutions that improve manufacturing processes, supply planning, material availability, supplier performance, quality, and operational decision-making.
  • Develops predictive and intelligence capabilities that help identify shortages, production risks, process issues, and planning constraints.
  • Partners with operational leaders and technical teams to embed AI into existing workflows and systems.
  • Ensures solutions include appropriate validation, user review, and human approval for important operational decisions.

Sales and Customer-Facing Solutions

  • Designs AI-enabled solutions that improve sales processes, customer service, response speed, information access, and decision support.
  • Identifies opportunities to automate repetitive work, improve workflow accuracy, and provide employees with timely, relevant information.
  • Partners with commercial and customer-facing teams to convert business needs into practical and measurable AI use cases.
  • Drives adoption by ensuring solutions are usable, reliable, and integrated into established business processes.

Enterprise Integration and Platform Alignment

  • Designs integrations between AI solutions and enterprise applications, data platforms, business systems, and workflow tools.
  • Develops solutions within the platform direction and technical standards established by the Office of AI.
  • Promotes the use of reusable architecture patterns, shared services, common components, and approved development approaches.
  • Works with Information Technology teams to address infrastructure, identity, access, data, security, and support requirements.

Technical Collaboration and Delivery Support

  • Provides technical direction and coaching to developers, vendors, and implementation partners without direct supervisory responsibility.
  • Partners with the Senior Director of AI and the Office of AI to ensure solutions align with enterprise strategy, governance, and platform decisions.
  • Collaborates with business owners, data teams, cybersecurity, application teams, and infrastructure teams throughout delivery.
  • Explains technical decisions, tradeoffs, dependencies, and risks in clear business language.

Business Value and Continuous Improvement

  • Works with business owners to define success measures before solutions are developed.
  • Evaluates results such as cycle-time reduction, employee effort saved, improved accuracy, adoption, risk reduction, and financial impact.
  • Recommends whether solutions should be expanded, redesigned, reused, or discontinued based on results.
  • Monitors developments in AI technologies and recommend practical applications aligned with enterprise priorities.

Qualifications and Skills:

  • Bachelor's degree in computer science, engineering, data science, information systems, or a related field.
  • A minimum of 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 and 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, access management, and cloud architecture.
  • Experience with the practices for deploying, monitoring, maintaining, and improving AI and machine-learning solutions.
  • Ability to evaluate technical options and explain architecture decisions, business implications, risks, and tradeoffs clearly.
  • Experience working across internal teams, shared services, vendors, and other delivery partners.
  • Demonstrated ability to manage multiple technical priorities in a complex enterprise environment.

Preferred Qualifications:

  • 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.

J.B. Poindexter & Co., Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

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Role Details

Title Senior AI Architect
Location Houston, TX, US
Category AI Architect
Experience Senior
Salary Not disclosed
Remote No

About This Role

This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.

The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.

Across the 11,520 AI roles we're tracking, AI Architect positions make up 1% of the market. At JB Poindexter & Co, this role fits into their broader AI and engineering organization.

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

What the Work Looks Like

Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

Skills Required

Azure (8% of roles) Python (20% of roles)

Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.

Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.

Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.

Compensation Benchmarks

AI Architect roles pay a median of $224,640 based on 245 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.

Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.

JB Poindexter & Co AI Hiring

JB Poindexter & Co has 1 open AI role right now. They're hiring across AI Architect. Based in Houston, TX, US.

Location Context

Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).

Career Path

Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.

From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.

Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.

What to Expect in Interviews

AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.

When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.

AI Hiring Overview

The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.

The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

The AI Job Market Today

The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.

The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.

AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.

Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.

The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.

Frequently Asked Questions

Based on 245 roles with disclosed compensation, the median salary for AI Architect positions is $224,640. Actual compensation varies by seniority, location, and company stage.
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
About 6% of the 11,520 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
JB Poindexter & Co is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI Architect positions include Senior Engineer, AI Architect, Engineering Manager, Principal Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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