Part-Time AI Architect - 1703

Scientific Applications & Research Associates (SARA), Inc.•Colorado Springs, CO

5d•$70 - $110•Onsite

About The Position

Scientific Applications & Research Associates, Inc. (SARA) is hiring a part-time AI Architect for its High Power Electromagnetics Division in Colorado Springs, Colorado. The position will work alongside the current AI Architect to expand adoption of secure, practical AI solutions across business and technical functions. It combines hands-on architecture and implementation with technical leadership, emphasizing secure onsite and 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. The role owns assigned initiatives from requirements definition through deployment and continued improvement, collaborating with the current AI Architect, executive leadership, IT, cybersecurity, data owners, engineering teams, and external partners. Success is measured by adoption, realized business value, and reliable, sustainable production operations. The role is based on-site in Colorado Springs. Work takes place in indoor laboratory and office settings and outdoor field test environments, where moderate physical hazards may exist. The position may require moderate physical effort, including standing, sitting, using hands, and carrying or lifting up to 50 pounds. The projected compensation range is $70 to $110 per hour. The application deadline is November 5, 2026.

Requirements

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 or more 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 deploying LLMs using open-source and/or proprietary models.
Experience with RAG architectures, embedding models, and vector databases.
Experience with AI infrastructure in on-premises, private cloud, or hybrid environments.
Experience with enterprise data systems, including SQL databases, document repositories, and APIs.
Experience with authentication, authorization, and secure application integration.
Experience evaluating and monitoring 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 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.
Must be a U.S. citizen and able 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.
Work is performed in indoor laboratory and office settings and outdoor field test environments; moderate physical hazards may exist.

Nice To Haves

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 applications.
AI product management or AI platform ownership experience.
Technically credible and hands-on.
Collaborative, able 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.

Responsibilities

Partner with the current AI Architect to advance 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 RAG pipelines, enterprise data integrations, model hosting, and inference workflows.
Develop secure integrations with enterprise applications, databases, and services, including MCP interfaces where appropriate.
Contribute reusable platform capabilities, integration patterns, and deployment practices to 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 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.