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About This Role
As a member of the Professional Services team, AI Architects help position, sell, and support our platform as the gold standard for AI in enterprises and overcome any barriers to delivering for customers. You will work hand\-in\-hand with Customer Success, Sales, Product, Engineering, and Marketing to help us bring our platform to clients and prospects in the public sector space.
You will be responsible for providing the technical expertise to support the installation, configuration, upgrading, and integration of our products into customer environments. You will have a broad range of skills and experience spanning systems architecture, ETL flow design, security engineering, cloud computing, Kubernetes orchestration, and more. You will have demonstrated experience deploying and maintaining technical platforms in both unclassified and classified environments, navigating federal security and compliance requirements, and responding to Common Vulnerabilities and Exposures (CVEs) to ensure platform integrity and operational continuity. You will have the insight to make the connection between a customer s specific business problems and our solution and the customer\-facing skills to communicate that connection and vision to a wide variety of technical and executive audiences. Furthermore you will have the skills to provide consultative assistance on architecture and implementation of the customer s business problem, usually through partnership with IT teams within federal government agencies.
As the AI Architect for the Federal Sector, you will also serve as a liaison between Product, Engineering, and the Federal business. You will apply your expertise in Federal infosec requirements, the Risk Management Framework, and FedRAMP and Agency authorization processes to inform how the Product evolves to meet Federal requirements and maintain continuous Authority to Operate (ATO).
Requirements:
- Shares our passion for delivering AI and machine learning for enterprises and thrives in the dynamic AI environment.
- Lead the professional services effort to plan and execute successful installations, upgrades, and security accreditation in Federal customer base, including managing CVE remediation and timely patch response within classified and sensitive environments.
- Have the flexibility and willingness to jump in and get what needs to be done to make us and our customers successful.
- Provide consultative assistance on architecture and implementation.
- Deploy and operate solutions within FedRAMP Authorized, IL5, and IL6 classified environments, ensuring compliance with applicable security controls, continuous monitoring requirements, and CVE response SLAs.
- Support pre\-sales activities including architectural overviews, security review, and scoping of professional services activities.
- Keep up to date on the ever\-evolving technologies for systems architecture, AI/ML, and LLM/MLOps to be an authoritative resource for both internal and customers.
- Work collaboratively with a broad range of people inside and outside the company.
- May support, oversee, and/or train technical members of partner organizations as it pertains to architecture, administration, and maintenance of the platform.
- This opportunity is contingent on security clearance validation.
Expertise:
- Federal Domain Mastery: 7\+ years in Federal engineering with 3\+ years supporting IL5/IL6\+ environments. You must have a proven track record navigating the full ATO lifecycle, managing RMF processes, and handling Continuous Monitoring (ConMon) for FedRAMP and Agency\-specific authorizations.
- Kubernetes and Secure Enclaves: Deep production expertise with AKS, EKS, or OpenShift. You are an expert at architecting and maintaining production\-grade clusters in air\-gapped, egress\-constrained, or "high\-side" disconnected environments (CKA/CKAD preferred).
- Infrastructure as Code (IaC): Advanced proficiency with Terraform/OpenTofu for immutable infrastructure provisioning and Ansible/Puppet for automated configuration management and drift detection.
- Linux Hardening \& SecOps: Hands\-on mastery of Linux systems with a focus on STIG/CIS hardening, automated patch management, and rapid CVE/vulnerability triage within secure enclaves.
- System Administration: Operational support for production systems covering for system administration, integrations, monitoring, capacity planning, user provisioning, and coordination of maintenance windows.
- Modern DevSecOps: Demonstrated experience building and maintaining CI/CD pipelines on K8s using tools like GitLab, Helm, Flux, or Argo.
- AI/ML Infrastructure: Understanding of the infrastructure requirements for AI/ML workloads, including GPU partitioning/scheduling, model serving architecture, and data egress challenges in a federal context.
- Strategic Communication: Excellent oral and written skills, with the ability to translate complex technical risks into actionable insights for technical teams and executive stakeholders.
- Education: Bachelors in Computer Science, a related technical field, or equivalent high\-level work experience.
- Clearance: Active Secret clearance (required); TS/SCI (highly preferred).
- Logistics: Willingness to be on\-site at local (DC area) client facilities up to 50% during peak project phases, with approximately 20% regional travel.
Bonus points:
- Certified Kubernetes Administrator
- AWS Certified Solutions Architect or AWS Certified DevOps Engineer certification.
Citizenship and Clearance Requirements:
- Must have an active Secret clearance
For consideration, please submit your resume as a MS Word attachment to [email protected]
The Consortium
"Combining Talent with Technology"
www.consortiuminc.com
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 3,823 AI roles we're tracking, AI Architect positions make up 1% of the market. At The Consortium 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 $212,500 based on 108 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
The Consortium Inc. AI Hiring
The Consortium Inc. has 1 open AI role right now. They're hiring across AI Architect. Based in Washington, DC, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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