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About This Role
Job Family:
Data Science Consulting Travel Required:
Up to 10% Clearance Required:
Ability to Obtain Public Trust
The AI Architect will play a critical technical leadership role within Guidehouse’s AI \& Data practice. This role is responsible for architecting and delivering AI prototypes and enterprise\-grade solutions, supporting the firm’s AI Studio, and serving as a technical lead for strategic AI initiatives. The AI Architect will partner closely with business, product, delivery, IT, and security stakeholders to design solutions that solve business problems, create measurable value, and operate within Guidehouse enterprise environments and controls. This role requires hands\-on technical leadership across Generative AI, Agentic AI, cloud\-native application delivery, and data solution development, while mentoring developers and promoting engineering excellence across delivery teams.
What You Will Do:
- Architect and implement enterprise\-grade AI solutions by collaborating with senior leadership to define technology strategies aligned with business objectives.
- Serve as a technical leader for strategic AI initiatives, guiding solution design decisions across architecture, platforms, and implementation approaches.
- Support and help advance AI Studio capabilities, including reusable architectures, accelerators, best practices, and reference implementations.
- Lead the design and hands\-on development of AI platforms, pipelines, and GenAI / Agentic AI solutions, including LLM\-based systems, RAG, MLOps, cloud\-native deployment, and scalable agentic architectures.
- Leverage Guidehouse cloud and development environments, including AWS, Azure, GCP, Databricks, and related enterprise platforms, to build secure, scalable solutions that can move from MVP to production.
- Apply strong software engineering fundamentals and modern development practices, including CI/CD, infrastructure\-as\-code, environment automation, code quality, and maintainable solution design.
- Partner with cross\-functional teams, including IT, Security, and business stakeholders, to identify high\-impact AI use cases and deliver MVPs and production\-grade solutions.
- Provide technical leadership, coaching, and mentorship to AI engineers, acting as a technical lead within a delivery cell while communicating architectural decisions, trade\-offs, risks, and recommendations clearly to technical and non\-technical stakeholders.
What You Will Need:
- Bachelor’s degree from an accredited college/university.
- Based on our contractual obligations, candidate must be located within the United States and US Citizen.
- Must be able to OBTAIN and MAINTAIN a Federal or DoD "PUBLIC TRUST".
- Minimum 8 years of hands\-on experience in AI, machine learning, data analytics, software engineering, cloud engineering, or solution architecture.
- Minimum 2 years of hands\-on experience with Generative AI and/or Agentic AI architectures and solutions.
- Proven experience architecting, building, and deploying AI, data, or cloud\-native solutions on enterprise platforms such as AWS, Azure, GCP, Databricks, or related technologies.
- Strong understanding of AI/ML fundamentals, including GenAI patterns such as RAG, agents, orchestration, prompt engineering, model selection, and evaluation trade\-offs.
- Relevant AI, cloud, or data/industry certifications.
- Strong software engineering fundamentals, including modern development practices such as CI/CD, infrastructure\-as\-code, environment automation, code quality, and maintainable solution design.
- Understanding of data pipelines, data quality, data governance, privacy considerations, security controls, compliance expectations, and responsible AI requirements.
- Ability to design and deliver solutions within enterprise cloud, development, security, compliance, and IT process constraints.
- Strong communication, collaboration, and technical leadership skills, with the ability to mentor developers, influence without direct authority, and partner effectively with product, delivery, IT, security, and business stakeholders.
What Would Be Nice To Have:
- Experience working in a consulting or advisory capacity, supporting client\-facing AI and data initiatives.
- Master’s degree in a relevant technical or business discipline.
- Certifications in cloud architecture, AI/ML engineering, data engineering, cybersecurity, or data science.
- Experience contributing to AI thought leadership, accelerators, reference architectures, or enterprise\-level strategic initiatives, such as an AI Center of Excellence or AI Transformation program.
- Experience mentoring or leading distributed teams in a global delivery model.
The annual salary range for this position is $149,000\.00\-$248,000\.00\. Compensation decisions depend on a wide range of factors, including but not limited to skill sets, experience and training, security clearances, licensure and certifications, and other business and organizational needs. What We Offer:
Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace.
Benefits include:
- Medical, Rx, Dental \& Vision Insurance
- Personal and Family Sick Time \& Company Paid Holidays
- Position may be eligible for a discretionary variable incentive bonus
- Parental Leave and Adoption Assistance
- 401(k) Retirement Plan
- Basic Life \& Supplemental Life
- Health Savings Account, Dental/Vision \& Dependent Care Flexible Spending Accounts
- Short\-Term \& Long\-Term Disability
- Student Loan PayDown
- Tuition Reimbursement, Personal Development \& Learning Opportunities
- Skills Development \& Certifications
- Employee Referral Program
- Corporate Sponsored Events \& Community Outreach
- Emergency Back\-Up Childcare Program
- Mobility Stipend
About Guidehouse
Guidehouse is an Equal Opportunity Employer–Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation.
Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco.
If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1\-571\-633\-1711 or via email at [email protected]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation.
All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains including @guidehouse.com or [email protected]. Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process.
If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse’s Ethics Hotline. If you want to check the validity of correspondence you have received, please contact [email protected]. Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant’s dealings with unauthorized third parties.
*Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.*
Salary Context
This $149K-$248K range is above the median 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 Guidehouse, 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. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($198K) sits 8% below the category median. Disclosed range: $149K to $248K.
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.
Guidehouse AI Hiring
Guidehouse has 5 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span Washington, DC, US, New York, NY, US, Austin, TX, US. Compensation range: $124K - $248K.
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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