Interested in this AI Architect role at Jabil?
Apply Now →About This Role
At Jabil (NYSE: JBL), we are proud to be a trusted partner for the world's top brands, offering comprehensive engineering, supply chain, and manufacturing solutions. With 60 years of experience across industries and a vast network of over 100 sites worldwide, Jabil combines global reach with local expertise to deliver both scalable and customized solutions. Our commitment extends beyond business success as we strive to build sustainable processes that minimize environmental impact and foster vibrant and diverse communities around the globe.
JOB SUMMARY
The Sr. Principal AI Architect is an industry-recognized technology leader with deep expertise in Artificial Intelligence (AI), enterprise architecture, intelligent automation, and digital transformation. You will serve as a key technical leader responsible for helping define, develop, and implement Jabil's Enterprise AI strategy, architecture, and adoption roadmap across the organization.
Accomplished in leading multiple technical teams through architecture definition, solution development, deployment, and operationalization, the Sr. Principal AI Architect provides strategic direction for AI initiatives involving Generative AI, Computer Vision, Digital Twins, Machine Learning, intelligent automation, and emerging AI technologies. You will partner with business, engineering, manufacturing, quality, supply chain, and IT leaders to identify opportunities where AI can deliver measurable business value while establishing standards, architectures, governance, and best practices across Jabil.
You will consult on enterprise AI architectures, assesses technical requirements and organizational capabilities needed to accelerate AI adoption, and provides technical leadership to ensure AI solutions are scalable, secure, responsible, and aligned with business objectives.
ESSENTIAL DUTIES AND RESPONSIBILITIES
- Meets the requirements of key technology Lead Engineer in respective expertise.
- Serves as a senior technical leader in the development and execution of Jabil's Enterprise AI strategy and roadmap.
- Leads the development and implementation of enterprise AI architectures, platforms, and frameworks supporting Generative AI, Computer Vision, Digital Twins, Machine Learning, and intelligent automation initiatives.
- Acts as a trusted technical advisor to executive leadership, business stakeholders, and engineering organizations regarding AI strategy, capabilities, investments, and adoption plans.
- Works with Project Manager Engineering Services and leadership teams to assess technical feasibility, capabilities, risks, and investment requirements for AI initiatives and technology opportunities.
- Provides technical input to identify and solve project risks that may affect enterprise AI programs, strategic initiatives, overall project plans, and schedules.
- Leads the development and utilization of enterprise standards, governance frameworks, best practices, and processes for AI solution design, deployment, monitoring, and lifecycle management.
- Stays abreast of latest advancements in Artificial Intelligence, Generative AI, Computer Vision, Digital Twins, Cloud AI platforms, intelligent automation, and emerging technologies to ensure Jabil remains competitive and innovative.
- Raises perception of Jabil's AI and technology leadership within the industry by authoring or presenting papers, articles, patents, technical publications, white papers, conference presentations, and other thought leadership materials.
- Maintains active participation in relevant industry organizations, technology councils, standards bodies, and AI-focused communities.
- Works with Site Directors, Business Unit leaders, and executive stakeholders to define R&D initiatives, pilot programs, and strategic investments that expand Jabil's AI capabilities and competencies.
- Leads the development and implementation of enterprise AI platform architectures in collaboration with senior engineering, data, and technology staff.
- Guides and oversees the development of AI system requirements, data architectures, interfaces, governance models, integration standards, and technical specifications necessary to maintain consistency and integrity across enterprise AI solutions.
- Defines reference architectures for Generative AI, AI Agents, Digital Twin platforms, Computer Vision solutions, MLOps environments, and enterprise AI systems.
- Manages solution architecture and technical documentation at the enterprise, platform, and system levels.
- Guides and assists Jabil leadership in evaluating new AI technologies, platforms, infrastructure, and deployment strategies.
- Demonstrates key leadership and serves as a senior technical leader for local and remote development, engineering, data science, and AI teams.
- Identifies and develops innovative solutions that enhance Jabil's capabilities through AI, automation, simulation, predictive analytics, and intelligent decision support systems.
- Oversees technical teams involved in proof-of-concepts, experimentation, model evaluation, benchmarking, Digital Twin development, Computer Vision deployments, and enterprise AI initiatives.
- Leads and/or supports technology training, AI adoption programs, transfer of best practices, and capability development across Jabil facilities globally.
- Effectively consults on multiple strategic initiatives and technology programs in parallel.
- Defines objectives and evaluation methods for experimentation, model validation, performance analysis, AI effectiveness measurement, and business value realization.
- Drives the development of intellectual property, patents, technical publications, and white papers related to AI and emerging technologies.
- Establishes and promotes responsible AI, security, governance, compliance, and risk management practices across enterprise AI initiatives.
- May perform other duties and responsibilities as assigned.
JOB QUALIFICATIONS
LANGUAGE SKILLS
Ability to read, analyze, and interpret professional journals, technical publications, business materials, industry reports, technical procedures, and governmental regulations. Ability to author technical publications, white papers, strategy documents, business cases, and enterprise architecture standards. Ability to effectively present technical concepts and AI strategies to executives, engineers, business leaders, customers, and stakeholders at all organizational levels.
MATHEMATICAL SKILLS
Ability to work with advanced mathematical and statistical concepts including probability, predictive analytics, machine learning methodologies, data modeling, optimization techniques, and quantitative evaluation methods. Ability to apply mathematical concepts to practical business and engineering applications.
REASONING ABILITY
- Ability to define complex business and technical problems, collect and analyze data, establish facts, and draw valid conclusions.
- Ability to interpret a broad range of technical, architectural, operational, and business requirements and transform them into scalable AI and digital transformation solutions.
- Ability to develop analytical models, AI strategies, and enterprise architectures that solve business problems and create measurable value.
- Ability to apply advanced concepts related to machine learning, computer vision, generative AI, data science, systems engineering, experimentation, simulation, optimization, and enterprise architecture.
- Ability to evaluate emerging technologies and determine their applicability, scalability, and business impact across diverse manufacturing and enterprise environments.
EDUCATION & EXPERIENCE REQUIREMENTS
Bachelor's degree in Engineering, Computer Science, Data Science, Artificial Intelligence, Applied Mathematics, or a related technical discipline. Plus:
- Master's degree preferred.
- Minimum of 15 years (Bachelor's degree) or 8 years (Master's degree) of progressively increasing technical leadership experience.
- Experience influencing executive stakeholders and driving enterprise-wide technology adoption initiatives.
- Demonstrated innovation through patents, publications, white papers, industry presentations, or recognized technical leadership.
- Proven ability to lead geographically distributed technical teams and strategic initiatives.
- Or an equivalent combination of education, training, and experience.
TECHNICAL REQUIREMENTS
Demonstrated experience developing enterprise architectures, technology strategies, or large-scale software, data, cloud, or AI platforms. And experience leading enterprise AI initiatives involving one or more of the following:
- Generative AI
- Large Language Models (LLMs)
- AI Agents
- Computer Vision
- Digital Twin
- Machine Learning
- Intelligent Automation
Predictive Analytics *
This position is not eligible for immigration and/or visa sponsorship. Applicants must be legally authorized to work in the United States without current or future sponsorship by Jabil. Applicants requiring sponsorship will not be considered.
BE AWARE OF FRAUD: When applying for a job at Jabil you will be contacted via correspondence through our official job portal with a jabil.com e-mail address; direct phone call from a member of the Jabil team; or direct e-mail with a jabil.com e-mail address. Jabil does not request payments for interviews or at any other point during the hiring process. Jabil will not ask for your personal identifying information such as a social security number, birth certificate, financial institution, driver’s license number or passport information over the phone or via e-mail. If you believe you are a victim of identity theft, contact the Federal Bureau of Investigations internet crime hotline (www.ic3.gov), the Federal Trade Commission identity theft hotline (www.identitytheft.gov) and/or your local police department. Any scam job listings should be reported to whatever website it was posted in. Jabil, including its subsidiaries, is an equal opportunity employer and considers qualified applicants for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, age, disability, genetic information, veteran status, or any other characteristic protected by law.
Accessibility Accommodation
If you are a qualified individual with a disability, you have the right to request a reasonable accommodation if you are unable or limited in your ability to use or access Jabil.com/Careers site as a result of your disability. You can request a reasonable accommodation by sending an e-mail to [email protected] or calling 727-803-7988 with the nature of your request and contact information. Please do not direct any other general employment related questions to this e-mail or phone number. Please note that only those inquiries concerning a request for reasonable accommodation will be responded to.
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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 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At Jabil, 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 in Demand for This Role
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 $224,070 based on 236 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Jabil AI Hiring
Jabil has 1 open AI role right now. They're hiring across AI Architect. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
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