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About This Role
At Infoblox, every breakthrough begins with a bold “what if.”
What if your ideas could ignite global innovation?
What if your curiosity could redefine the future?
We invite you to step into the next exciting chapter of your career journey. Bring your creativity, drive, your daring spirit, and feel what it’s like to thrive on a team big enough to make an impact, yet small enough to make a difference. Our cloud\-first networking and security solutions already protect 70% of the Fortune 500 , and we’re looking for creative thinkers ready to push that influence even further. Join us and discover how far your bold “what if” can take the world, your community, and your career.
How we empower our people is extraordinary: we’re recognized as a Glassdoor Best Place to Work 2025, Great Place to Work\-Certified in five countries, and honored by Cigna as a Healthy Workforce honors for three consecutive years; and what we build is world class: named CybersecAsia’s Best in Critical Infrastructure 2024 — clear evidence that when first\-class technology meets empowered talent, remarkable careers take shape. So, what if the next big idea, and the next great career story, comes from you? Become the force that turns every “what if” into “what’s next.”
In a world where you can be anything, Be Infoblox.
Principal AI Architect
We have an opportunity for a Principal AI Architect to join our Product organization in United States, reporting to the Vice President \- Products. In this pivotal role, you will define the architectural direction for next\-generation AI data centers and AI platforms — from GPU clusters and inference platforms to autonomous agent systems and multi\-cloud AI deployments. Collaborating closely with Product Management, Engineering, Strategic Alliances, and Customer Engineering, you will create reference architectures, best practices, and customer\-facing guidance that shape how enterprises design, operate, and secure AI infrastructure and networking at scale.
Be a Contributor — What You’ll Do
- Define AI Infrastructure Architecture. You will create comprehensive architectural blueprints for modern AI infrastructure spanning AI factories, GPU clusters, distributed training environments, LLM inference platforms, and autonomous AI systems across hybrid and multi\-cloud environments. You'll balance performance, resiliency, operational efficiency, and security in every design, and partner with Product Management and Engineering to influence long\-term platform strategy.
- Architect AI\-Scale Networking. You will design and document networking architectures that support thousands of GPUs, with deep expertise across high\-performance interconnects, lossless Ethernet fabrics, and AI traffic engineering patterns. You'll define best practices for maximizing throughput, resiliency, and operational simplicity in the most demanding AI environments in the world.
- Guide AI Platform and Inference Architecture. You will develop integration patterns across major AI orchestration platforms including Kubernetes\-based environments, Slurm\-managed clusters, and vendor\-specific AI enterprise stacks and produce reference architectures for production\-grade AI serving and inference platforms optimized for latency, scalability, and high availability.
- Drive Infrastructure Automation. You will champion automation across the AI infrastructure lifecycle, including provisioning, cluster deployment, configuration management, GitOps workflows, and AI operations (AIOps), helping customers and internal teams move from manual operations to self\-managing, observable infrastructure.
- Lead Customer Architecture Engagements. You will serve as the principal technical advisor for strategic AI infrastructure initiatives, leading engagements with Chief Architects, CTOs, AI Platform Engineering teams, and Enterprise Architecture organizations — supporting customers from early design through production deployment.
- Build Strategic Partnerships. You will collaborate with leading AI infrastructure vendors and cloud providers including NVIDIA, AMD, Cisco, Arista, AWS, Microsoft Azure, Google Cloud, and others to develop joint reference architectures and validate interoperability across the AI ecosystem.
- Represent Infoblox as an Industry Leader. You will publish reference architectures, technical whitepapers, design guides, and blog posts, and represent Infoblox at major industry conferences and standards organizations. You will continuously evaluate emerging technologies including agentic AI, distributed AI systems, and autonomous infrastructure and translate them into practical architectural guidance and product strategy.
Be Prepared — What You Bring
- 15\+ years designing enterprise networking, cloud infrastructure, or AI platform architectures, with deep expertise in modern data center networking and distributed systems
- Strong hands\-on experience with GPU infrastructure, cloud\-native platforms, and AI/ML infrastructure
- Solid understanding of high\-performance networking technologies including Ethernet, InfiniBand, RoCEv2, RDMA, EVPN/VXLAN, and BGP at large scale
- Experience influencing technical strategy across engineering, product, and executive stakeholders
- Excellent written and verbal communication skills — you can translate complex architectural decisions into clear guidance for any audience
- Bachelor’s degree in Computer Science, Engineering, Information Technology, or equivalent practical experience
- Preferred :
+ Hands\-on experience with NVIDIA DGX, SuperPOD, Spectrum\-X, AI Enterprise, or Mission Control platforms
+ Experience with AI orchestration and inference platforms such as Slurm, Kubeflow, Ray, KServe, Triton, TensorRT\-LLM, vLLM, or NVIDIA NIM
+ Familiarity with observability platforms, GitOps, Infrastructure as Code, OpenTelemetry, Cilium, and eBPF
+ Knowledge of agentic AI platforms, AI gateways, distributed AI systems, and emerging AI networking technologies
+ Demonstrated thought leadership through technical publications, conference presentations, or open\-source contributions
Be Successful — Your Path
First 90 Days: Immerse in our culture, connect with mentors (Blox Buddies), and map the systems and meet with key stakeholders that rely on your work. Discuss and create short/long term goals.
Six Months: Own the development of at least one major reference architecture or customer\-facing technical deliverable. Establish working relationships with key strategic partners. Begin representing Infoblox in customer architecture engagements alongside senior teammates.
One Year: Serve as the primary technical voice for AI infrastructure architecture at Infoblox leading customer engagements independently, publishing externally, shaping product strategy, and influencing the broader industry narrative around AI infrastructure
Belong — Your Community
Our culture thrives on inclusion, rewarding the bold ideas, curiosity, and creativity that move us forward. In a community where every voice counts, continuous learning is the norm. So, whether you code, create, sell, or care for customers, you’ll grow and belong here.
Be Rewarded — Benefits That Help You Grow, Thrive, Belong
- Comprehensive health coverage, generous PTO, and flexible work options
- Learning opportunities, career\-mobility programs, and leadership workshops
- Sixteen paid volunteer hours each year, global employee resource groups, and a “No Jerks” policy that keeps collaboration healthy
- Modern offices with EV charging, healthy snacks (and the occasional cupcake), plus hackathons, game nights, and culture celebrations
- Charitable Giving Program supported by Company Match
- We practice pay transparency and reward performance. Offers reflect role location, internal equity, experience, skills, education, and certifications. Base salary for this position: USD 195,000 \- 300,000 plus corporate bonus
Ready to Be the Difference?
*Infoblox is an Affirmative Action and Equal Opportunity Employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, national origin, genetic information, age, disability, veteran status, or any other legally protected basis*
\#LI\-HK1
\#LI\-Remote
Salary Context
This $195K-$300K range is above the 75th percentile for AI Architect roles in our dataset (median: $197K across 33 roles with salary data).
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 3,708 AI roles we're tracking, AI Architect positions make up 1% of the market. At Infoblox, 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 $254,798 based on 67 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $195K to $300K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Infoblox AI Hiring
Infoblox has 1 open AI role right now. They're hiring across AI Architect. Based in US. Compensation range: $300K - $300K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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