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About This Role
Scientific Applications & Research Associates, Inc. (SARA) is a world-class Defense Research and Development enterprise, with a charter to create new and emerging technological innovations in support of Commercial & Government organizations. We have expertise in RF, EMP, Pulsed Power, Acoustics and Threat Warning Systems, and we prioritize partnering with our customers to address real-world problems with cost-effective solutions… that work.
The SARA High Power Electromagnetics Division (HPEM) is looking for a remarkably talented, motivated, and imaginative AI Architect to join our growing enterprise AI capability at our Colorado Springs, CO location. Working alongside our current AI Architect, this individual will help expand and accelerate the adoption of secure, practical AI solutions across business and technical functions.
The role combines hands-on architecture and implementation with technical leadership, focusing on secure onsite/private LLM deployments, enterprise data integration, and production-ready AI workflows. The AI Architect will contribute to shared platform architecture, governance, evaluation standards, and production lifecycle management while taking ownership of assigned initiatives from requirements definition through deployment and ongoing improvement.
The AI Architect will collaborate with our current AI Architect, executive leadership, IT, cybersecurity, data owners, engineering teams, and external partners to deliver AI-enabled workflows that improve decision-making, automate routine analysis, and increase efficiency. Success will be measured by adoption, business value realization, and reliable, sustainable production operations.
Key Responsibilities
- Partner with the current AI Architect to advance the enterprise AI strategy, roadmap, and architectural standards
- Lead assigned AI initiatives from discovery and requirements definition through implementation, deployment, and ongoing support
- Work with business and technical stakeholders to identify, prioritize, and deliver high-value AI use cases
- Architect, implement, and maintain secure onsite and private LLM deployments.
- Evaluate and select AI tools, models, platforms, and architectures based on capability, security, maintainability, and cost
- Design and implement Retrieval-Augmented Generation (RAG) pipelines, enterprise data integrations, model hosting, and inference workflows
- Develop secure integrations with enterprise applications, databases, and services, including Model Context Protocol (MCP) interfaces where appropriate
- Contribute to reusable platform capabilities, integration patterns, and deployment practices that enable adoption across additional business areas
- Partner with IT and cybersecurity to ensure AI systems meet applicable security, privacy, access control, and governance requirements
- Establish and apply evaluation methods for model quality, retrieval accuracy, reliability, performance, and business outcomes
- Develop and maintain AI usage guidelines, guardrails, and technical documentation in collaboration with the current AI Architect
- Support internal training, user onboarding, and adoption efforts
- Monitor deployed AI systems and improve performance, usability, resource utilization, and business value over time
Required Qualifications
- Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field
- 8 – 10 years of professional experience in AI, software engineering, data science, enterprise systems, or a related technical field
- 5+ years of experience leading complex technical initiatives, including architecture and delivery responsibilities
- Demonstrated hands-on experience deploying, integrating, and maintaining AI systems
- Proven ability to deliver and support systems beyond proof-of-concept
- Strong understanding of LLMs and generative AI concepts, capabilities, limitations, and architectural tradeoffs
- Experience with: + LLM deployment using open-source and/or proprietary models + RAG architectures, embedding models, and vector databases + AI infrastructure in on-premises, private cloud, or hybrid environments + Enterprise data systems, including SQL databases, document repositories, and APIs + Authentication, authorization, and secure application integration + Evaluation and monitoring of LLM applications and supporting infrastructure
- Proficiency in Python or another programming language used to build AI applications and integrations
- Familiarity with AI tooling ecosystems such as Open WebUI, LangChain, or LlamaIndex
- Familiarity with MCP and client-server architecture
- Familiarity with model lifecycle management and the maintenance of RAG data sources and retrieval pipelines
- Familiarity with authentication and security technologies such as JWT, OAuth, LDAP, and API key management
- Understanding of GPU infrastructure, inference performance, capacity planning, and cost tradeoffs
- Strong data governance and security awareness
- Ability to collaborate with another senior architect, align on shared standards, and independently execute assigned initiatives
- Strong communication skills across technical and non-technical audiences
- U.S. citizen with the ability to obtain and maintain a DoD security clearance
- May require moderate physical effort, including standing, sitting, use of hands while performing duties, and carrying or lifting up to 50 pounds
- Working environment is indoor, moderately quiet, reasonably clean, well-lighted and well-ventilated; moderate physical hazards may exist
Preferred Qualifications
- Master’s degree or higher in a related field
- 10 – 12 years of relevant professional experience, including substantial experience deploying and maintaining AI systems
- Experience deploying onsite or air-gapped AI systems
- Background in Government Contracting and/or environments involving sensitive or proprietary information
- Experience delivering AI solutions across multiple business or engineering functions
- Experience with cloud platforms such as AWS, Azure, or GCP
- Experience with high-level IT network and systems architecture
- Experience with automated testing, CI/CD, and repeatable deployment practices for AI application
- AI product management or AI platform ownership experience
Key Attributes
- Technically credible and hands-on
- Collaborative, with the ability to share architectural leadership and build on established capabilities
- Self-directed and accountable for delivering assigned initiatives
- Pragmatic and focused on measurable outcomes
- Able to balance rapid delivery with security, reliability, and maintainability
- Trusted technical advisor to stakeholders and executive leadership
SARA offers a competitive benefits package to include
- Medical, dental, vision, disability, and life insurance; and other supplemental programs
- A generous PTO program
- Tuition & certification reimbursement
- Flexible work schedules
- Employee bonuses
- 401(k) match
- Employee Stock Ownership Plan (ESOP) Pay Range
Salary at SARA is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $145,000 to $180,000 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of SARA’s total compensation package for employees. Positions are on-site, unless indicated otherwise.
Application Deadline: November 9, 2026
SARA is an equal employment opportunity employer.
Salary Context
This $145K-$180K range is below the median for AI Architect roles in our dataset (median: $175K across 37 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 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At SARA 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 $224,070 based on 236 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($162K) sits 27% below the category median. Disclosed range: $145K to $180K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
SARA Inc AI Hiring
SARA Inc has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Research Engineer. Based in Colorado Springs, CO, US. Compensation range: $110K - $180K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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