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About This Role
Transforming the Future of Enterprise Planning
At o9, our mission is to be the Most Value\-Creating Platform for enterprises by transforming decision\-making through our AI\-first approach. By integrating siloed planning capabilities and capturing millions—even billions—in value leakage, we help businesses plan smarter and faster.
This not only enhances operational efficiency but also reduces waste, leading to better outcomes for both businesses and the planet. Global leaders like Google, PepsiCo, Walmart, T\-Mobile, AB InBev, and Starbucks trust o9 to optimize their supply chains.
Senior AI Architect
o9 is restructuring its technology organization around purpose\-built engineering groups, upgrading to modern tech stacks and AI\-first software to drive a streamlined, R\&D\-focused powerhouse. As part of this transformation, we are building a world\-class core AI function from the ground up to serve as the foundational intelligence layer across all product engineering domains.
This is a premier technical individual contributor (IC) role with the structural weight, seniority, and impact of an SVP. We are looking for someone who has lived on the bleeding edge of artificial intelligence at industry\-defining technology companies—someone who can take a bold architectural vision and turn it into production reality.
The Scale of Opportunity
o9 is on a trajectory that very few enterprise software companies ever reach. We are building toward a global footprint of 10,000 enterprise customers—each running complex, mission\-critical supply chain and business planning operations on our platform. At that scale, AI is not a buzzword or a feature add\-on; it is our core strategic differentiator.
Our ambition goes further: o9 is pursuing a neurosymbolic AI architecture—combining the reasoning power of large language models with the formal rigor of symbolic computation and constraint\-based planning. The platform that emerges will be unlike anything currently available in enterprise software. The person who steps into this role will literally write the playbook and construct the foundation for this architecture.
To understand the core philosophy behind what you will be building, review the framework established by our EVP of AI, Ashwin Rao:
- Read the fundamental thesis on The Neurosymbolic Imperative for Enterprise AI.
- Explore the practical application of this paradigm in Davos Insights: Turning VUCA into Value with Neuro\-Symbolic AI.
The north star for our customers is the APEX operating model—a state of integrated, autonomous, AI\-driven planning that eliminates the friction between insight and action across the enterprise. This role is the architectural engine that makes APEX possible.
What you’ll own
You are single\-handedly accountable for o9’s core AI architectural integrity and our neurosymbolic roadmap. Your mandate covers the following pillars:
- Neurosymbolic Platform Architecture: Architect the hybrid systems that seamlessly blend LLMs with deterministic, constraint\-based planning engines, translating high\-level conceptual vision into concrete, scalable code and system designs.
- AI Strategy \& Governance: Set the technical standard for how AI is implemented across the entire engineering organization, ensuring framework consistency, performance optimization, and enterprise\-grade reliability.
- Cutting\-Edge R\&D Evaluation: Continually evaluate, benchmark, and integrate state\-of\-the\-art models, agentic workflows, and vector infrastructures into the core o9 platform.
- Technical Truth: Serve as the ultimate technical court of appeal for complex AI engineering roadblocks, deep triage, and algorithmic limitations.
What you’ll build
- The foundational architecture for o9’s neurosymbolic AI engine, successfully bridging the gap between connectionist (neural networks) and symbolic AI.
- The framework and blueprints that allow hundreds of product engineers to safely, predictably, and continuously ship AI\-driven capabilities.
- The next\-generation intelligence layer enabling 10,000 enterprise customers to run autonomous business planning at an order of magnitude more scale than we serve today.
What you’ll have
- Experience: 15\+ years of deep engineering background, with a distinguished track record of building and transforming advanced software platforms.
- Tier\-1 Tech Background: Significant engineering tenure at a premier technology organization (e.g., FAANG, Uber, Slack, Snowflake, Stripe, Datadog, or comparable).
- Proven AI Leadership: Clear track record as a principal or lead architect on production\-grade AI/ML systems at massive scale.
- Neurosymbolic Aptitude: Deep, hands\-on familiarity with both large language models (generative AI, embeddings, vector databases) and traditional, structured, constraint\-based symbolic computation.
- High Autonomy: Direct, fast, and exceptionally comfortable operating without bureaucratic approval chains. You move quickly, own your architectural decisions, and hold yourself accountable for outcomes.
This position at o9 Solutions has an annual salary range of $203,752\-$280,159\. Additionally, you may be eligible to participate in our medical, retirement, and other company\-sponsored benefits.### *\*\*The above information reflects the expected base salary range, although the lower and upper bounds may vary based on location, skills, experience, certifications, licenses, or other relevant factors.*
More about us…
At o9, transparency and open communication are at the core of our culture. Collaboration thrives across all levels—hierarchy, distance, or function never limit innovation or teamwork. Beyond work, we encourage volunteering opportunities, social impact initiatives, and diverse cultural celebrations.
With a $3\.7 billion valuation and a global presence across Dallas, Amsterdam, Barcelona, Madrid, London, Paris, Tokyo, Seoul, and Munich, o9 is among the fastest\-growing technology companies in the world. Through our aim10x vision, we are committed to AI\-powered management, driving 10x improvements in enterprise decision\-making. Our Enterprise Knowledge Graph enables businesses to anticipate risks, adapt to market shifts, and gain real\-time visibility. By automating millions of decisions and reducing manual interventions by up to 90%, we empower enterprises to drive profitable growth, reduce inefficiencies, and create lasting value.
*o9 is an equal\-opportunity employer that values diversity and inclusion. We welcome applicants from all backgrounds, ensuring a fair and unbiased hiring process. Join us as we continue our growth journey!*
Salary Context
This $203K-$280K range is above the 75th percentile for AI Architect roles in our dataset (median: $172K across 30 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 4,133 AI roles we're tracking, AI Architect positions make up 1% of the market. At o9 Solutions, 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 $215,000 based on 115 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($241K) sits 13% above the category median. Disclosed range: $203K to $280K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
o9 Solutions AI Hiring
o9 Solutions has 1 open AI role right now. They're hiring across AI Architect. Based in Dallas, TX, US. Compensation range: $280K - $280K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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