Interested in this AI Architect role at Great Dane?
Apply Now →Skills & Technologies
About This Role
Overview: THE ROLE
At Great Dane, we rely on makers and we’re calling on them to join our expanding team, the time has never been better to Embrace Your Great and work in an environment with the highest safety standards in the industry.
The Position: The AI Architect defines the technical architecture, standards, and reusable patterns for enterprise AI, machine learning, and generative AI solutions. This role ensures AI solutions are secure, governed, scalable, supportable, and aligned with enterprise data and application architecture. The AI Architect partners with Enterprise Architecture, Data Architecture, Data and AI Governance, and Application Development to translate approved AI opportunities into practical solution designs, implementation guidance, and supporting architecture artifacts.
Responsibilities: WHAT YOU’LL DO* Define the reference architecture for AI/ML and generative AI workloads, including the Durable Hub - Great Dane's central data and integration hub.
- Ensure enterprise data is AI-ready — well-governed, well-modeled, and accessible — by partnering closely with the Data Architect and Data Governance Committee.
- Partner with the Data Architect to ensure the enterprise ontology and semantic models are effective for AI consumption — defining how ontology concepts ground LLMs, RAG pipelines, and agent reasoning, and feeding gaps or ambiguities back into the ontology while the Data Architect retains ownership of its authoritative definition and governance.
- Evaluate, select, and integrate AI/ML platforms, frameworks, and services; define build-vs-buy guidance.
- Establish responsible AI practices, including governance, evaluation, monitoring, and controls for accuracy, bias, and data privacy.
- Design and prototype high-value AI use cases (e.g., predictive analytics, document/entity extraction, decision support) and partner with Development teams to shepherd them from proof of concept into production.
- Define MLOps/LLMOps patterns for model lifecycle, versioning, and observability, and collaborate with Development to embed them into delivery pipelines and engineering practices.
- Align AI architecture and delivery roadmaps with Intelligence Automation project owners and stakeholders to ensure initiatives support program priorities and deliver measurable value.
- Support Business Analysts in assessing and shaping AI solution opportunities by providing the technical perspective — evaluating feasibility, data readiness, implementation complexity, operational impact, and architectural fit — to inform business-value and roadmap decisions.
- Provide architecture direction, solution design guidance, and implementation artifacts to Development teams, partnering with engineers to co-design production-ready approaches while preserving Development ownership of engineering execution, integration, deployment, and operational support.
- Own and maintain the AI architecture documentation framework, including reference architectures, solution design documents, architecture decision records, technical standards, implementation patterns, integration diagrams, data flow diagrams, operational support models, and supporting artifacts required to guide delivery, governance, and long-term maintainability of AI solutions.
- Establish and govern documentation standards for AI solutions, ensuring architecture artifacts, technical designs, decision records, diagrams, and implementation guidance are created, maintained, versioned, and stored in approved enterprise repositories such as SharePoint and Miro.
- Provide technical mentorship on AI patterns to the data engineering and Development teams.
- Other duties as assigned.
Qualifications: COMPETENCIES:
- Transformation and Change Management: The ability to lead and execute initiatives that drive significant organizational change, including cultural, process, and structural transformations. Involves assessing readiness, creating a vision, aligning stakeholders, and applying structured change management frameworks.
- Operational Rigor: The disciplined application of structured processes, controls, and performance standards to ensure consistency, quality, and efficiency in operations. Focuses on accountability, compliance, and continuous improvement.
- Strategic Capability: The capacity to think critically, anticipate future trends, and develop strategies that position the organization for long-term success while balancing short- and long-term needs.
- Cultural Fit: The alignment of personal values, behaviors, and working style with the organization’s mission, vision, and culture. Adapts and thrives within the environment while constructively influencing its evolution.
- Ability to Build Sponsorship: The ability to gain the support, advocacy, and active involvement of key stakeholders for initiatives, strategies, or change efforts. Builds trust, demonstrates credibility, and aligns initiatives with stakeholder priorities.
YOUR SKILLS & ABILITIES (REQUIRED QUALIFICATIONS)
- Education: Bachelor's degree in Computer Science, Data Science, Machine Learning, or a related field; Master's preferred.
- Experience: 7+ years in software/data engineering or data science; 3+ years architecting and delivering production AI/ML or generative AI solutions. Demonstrated experience integrating AI with enterprise data platforms and deploying models at scale.
- Skills: Strong grounding in ML and generative-AI patterns (RAG, embeddings, model evaluation), MLOps/LLMOps, and cloud AI services (AWS preferred). Proficiency in Python and SQL; familiarity with streaming and lakehouse architectures (Kafka/Confluent, medallion, Iceberg). Working knowledge of data governance and responsible-AI principles. Excellent stakeholder communication. AWS certification a plus.
Travel: Occasional; typically less than 10%.
CAREER PATHWAYS:* Prerequisite Positions: Senior Data Scientist, ML Engineer, or Data/Solutions Architect.
- Potential Growth Opportunities: Principal AI Architect, Head of AI/ML, or Enterprise Architect.
PHYSICAL/MENTAL REQUIREMENTS:* Office and plant environment.
- Keyboarding, lifting, standing, bending, walking.
- Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
ABOUT GREAT DANE
With a history dating back to 1900, Great Dane has established itself as a transportation solutions leader of today – delivering innovative products and exceptional services that help our customers and employees succeed. We not only engineer durable and high-quality trailers and truck bodies; we foster relationships that last a lifetime. WHY WORK FOR US?
Great Dane is a leading manufacturer of dry-freight vans, refrigerated trailers, and flatbeds with 10 manufacturing facilities and 20 service centers throughout the United States. With a legacy like ours comes a dedication to the hard-working individuals that make Great Dane what it is today. Our jobs are waiting to be filled by driven, challenge-focused & enthusiastic people like you.
Our culture is comprised of “Champions” and offers a rewarding workplace rooted in core values of safety, leadership, integrity, quality, and passion. Every employee has opportunities to learn, grow, succeed and be engaged in a collaborative environment. BENEFITS OVERVIEW* Competitive compensation
- Benefits, including but not limited to dental, vision, and medical with employer contributions
- Retirement programs, including a Pension Plan and 401(k) Plan with employer match
- Tuition Reimbursement
- Paid holidays and vacation
- And more!
Great Dane is an Equal Opportunity Employer
Role Details
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 9,653 AI roles we're tracking, AI Architect positions make up 1% of the market. At Great Dane, 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
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 $230,000 based on 212 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Great Dane AI Hiring
Great Dane has 1 open AI role right now. They're hiring across AI Architect. Based in Savannah, GA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.