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About This Role
Senior Applied AI Architect
Req number:
R8009
Employment type:
Full time
Worksite flexibility:
Remote
Who we are
--------------
CAI is a global services firm with over 9,000 associates worldwide and a yearly revenue of $1\.3 billion\+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right—whatever it takes. Our tailor\-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.
Job Summary
CAI is hiring a Senior Applied AI Architect to serve as the primary technical engine behind our applied AI delivery across all four strategic motions. This is a builder’s role at the intersection of deep technical execution and strategic advisory
Job Description
We are seeking a highly skilled and experienced Senior Applied AI Architect to join our IT team. This position will be full\-time, remote, and is a salaried position.
What You'll Do
- Lead technical design for agentic AI systems, LLM integrations, MCP\-based orchestration, and multi\-model workflows in internal and external delivery environments
- Own build\-vs\-buy decisions for AI tooling across use cases, working with the CTO organization and design partners
- Develop reusable architectural patterns, integration blueprints, and technical playbooks that scale across CAI’s delivery organization
- Drive technical POC development from internal initiatives through to client\-ready case studies, architecture and implementation
- Serve as the technical lead and credibility anchor for AI solution conversations with executives, clients, and procurement stakeholders
- Work alongside functional and motion owners to determine whether and how AI applies, then own the technical approach and execution
- Pressure\-test AI feasibility on CTO assessments, POC scoping, and motion\-level AI roadmaps
- Mentor and develop junior AI technical staff; contribute to the Applied AI Delivery Team model
- Maintain deep, working knowledge of the enterprise AI landscape: LLMs (Anthropic Claude, OpenAI, Gemini), agentic frameworks, MCP architecture, Databricks, vector databases, and emerging tools
- Apply hands\-on fluency across the full stack CAI deploys; AI/ML frameworks, integration patterns, API design, cloud platforms (AWS, Azure), and security controls relevant to commercial and public sector public sector
- Ensure architectural choices align with CAI’s governance standards, NIST AI framework, GovRAMP requirements, and client\-specific compliance environments
- Bring an evidence\-based perspective on AI capability and limitations to keep initiatives grounded, realistic, and defensible
- Package internal AI delivery results into reusable inputs for client\-ready case studies, RFP content, and competitive positioning. Build the technical artifacts that move work forward: solution designs, architecture diagrams, integration specs, feasibility assessments, and deployment guides
What You'll Need
Required:
- 8\-10\+ years of experience in applied AI, enterprise software architecture, or AI/ML engineering with at least 3 years in a senior or lead role
- Demonstrated track record of designing and delivering production\-grade AI solutions end\-to\-end; hands\-on, not advisory
- Deep fluency with large language models, agentic AI frameworks, prompt engineering, and MCP or similar orchestration architectures
- Hands\-on experience with enterprise AI platforms: Anthropic Claude, OpenAI, Databricks, Azure AI / OpenAI Service, AWS Bedrock, or equivalent
- Strong system design and integration architecture skills: APIs, data pipelines, event\-driven systems, and cloud infrastructure
- Ability to operate credibly across technical depth (code environments, architecture review) and executive conversation
- Experience working in regulated or government\-adjacent environments, with familiarity with FedRAMP, GovRAMP, NIST, or SOC 2 compliance requirements
- Strong communication skills: able to translate complex technical architecture into executive\-ready artifacts and client\-facing narratives
- Bachelor's degree in computer science, Engineering, or a related technical field required; master's or advanced degree preferred; or a related field required; or the equivalent combination of education, technical certifications or training, or work experience
Preferred:
- Experience delivering AI solutions in state and local government, public sector managed services, or workforce services environments
- Hands\-on experience with Anthropic’s Model Context Protocol (MCP) and multi\-agent orchestration frameworks (LangChain, AutoGen, CrewAI, or equivalent)
- Track record of building reusable AI architectural assets, patterns, blueprints, frameworks, that scale beyond a single engagement
- Experience contributing to AI governance frameworks, responsible AI posture, or client\-facing compliance advisory
- Certifications in relevant platforms (AWS, Azure, Databricks, Google Cloud AI) or AI governance frameworks (ISACA, NIST AI RMF)
- Prior experience in a professional services or consulting delivery model with multiple concurrent client engagements.
Physical Demands
- Ability to safely and successfully perform the essential job functions consistent with the ADA and other federal, state, and local standards
- Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings, etc
- Ability to conduct repetitive tasks on a computer, utilizing a mouse, keyboard, and monitor
Reasonable accommodation statement
If you require a reasonable accommodation in completing this application, interviewing, completing any pre\-employment testing, or otherwise participating in the employment selection process, please direct your inquiries to application.accommodations@cai.io or (888\) 824 – 8111\.
EEO Statement
It is the policy of Computer Aid, Inc.(CAI) not to discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or because he or she is a protected veteran. It is also the policy of CAI to take affirmative action to employ and to advance in employment, all persons regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or protected veteran status, and to base all employment decisions only on valid job requirements. This policy shall apply to all employment actions, including but not limited to recruitment, hiring, upgrading, promotion, transfer, demotion, layoff, recall, termination, rates of pay or other forms of compensation and selection for training, including apprenticeship, at all levels of employment.
Employees and applicants of CAI will not be subject to harassment on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or because he or she is a protected veteran. Additionally, retaliation, including intimidation, threats, or coercion, because an employee or applicant has objected to discrimination, engaged or may engage in filing a complaint, assisted in a review, investigation, or hearing or have otherwise sought to obtain their legal rights under any Federal, State, or local EEO law is prohibited.
The pay range for this position is listed above. Exact compensation may vary based on several factors, including location, experience, and education. Benefit packages include medical, dental, and vision insurance, as well as 401k retirement account access. Employees in this role receive paid time off and may also be entitled to paid sick leave and/or other paid time off as provided by applicable law.
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 CAI (Computer Aid, Inc.), 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.
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.
CAI (Computer Aid, Inc.) AI Hiring
CAI (Computer Aid, Inc.) has 1 open AI role right now. They're hiring across AI Architect. Based in US.
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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