Senior AI Architect/Engineer

Technomics•Arlington, VA

22h•Onsite

About The Position

Technomics is a growing employee-owned, decision analytics company that specializes in cost and economic analysis to facilitate better decisions faster . We enable a wide range of clients across the Federal government, from senior level policy makers to program managers, to choose smartly, buy effectively and operate efficiently . We deliver practical, credible and defensible results offering actionable insights by applying data-driven and analytics-based approaches in combination with multidisciplinary talent, subject matter experts, and tangible and repeatable assets in the form of databases, models, approaches and techniques. Senior Analysts have the knowledge, skills, abilities and initiative to deliver timely, practical and innovative solutions to our clients as part of high-performing project teams typically composed of a mix of junior and mid-level analysts who will look to you for technical acumen and mentoring. Our employee-owners pride themselves on their ability to apply deep analytical rigor and innovative thought that assist clients in understanding and solving a myriad of challenging resource planning and management problems. This position is located in Arlington, VA. Position Overview The Senior AI Architect/Engineer will lead the design, buildout, and operationalization of the companys enterprise AI platform in Azure GCC High. The role will establish enterprise capabilities for secure frontier model access, RAG-enabled knowledge systems, and governed AI APIs available across the organization. The position will also enable internal engineering teams to deploy AI-powered tools, applications, and services within a controlled cloud environment. The architect will also apply their cloud and AI infrastructure expertise to support both internal initiatives and client-facing engagements.

Requirements

8+ years of experience in cloud architecture, platform engineering, or AI/ML infrastructure
Strong hands-on experience designing and deploying systems in Microsoft Azure
Experience building or operating AI/LLM enabled infrastructure, including familiarity with concepts such as RAG, embeddings, vector databases, and model APIs
Experience designing secure cloud architectures including identity integration, RBAC, private networking, and service-to-service authentication
Experience implementing monitoring, logging, and usage tracking for production cloud systems
Ability to translate business and operational requirements into practical cloud architecture and platform design decisions
Experience working with containerized services and modern deployment patterns (e.g., containers, APIs, managed services)
Strong written and verbal communication skills, including the ability to explain technical concepts to non-technical stakeholders

Nice To Haves

Experience working in Azure GCC High or other regulated government cloud environments
Strong familiarity with FedRAMP, DoD Impact Level (IL) requirements, CMMC, and NIST 800-171 security controls
Experience supporting U.S. Government or defense-sector clients
Experience building enterprise AI systems such as RAG-enabled applications or internal AI platforms
Relevant certifications such as Azure Solutions Architect (AZ-305), Azure Security Engineer (AZ-500), CISSP, CCSP, Security+

Responsibilities

Enterprise AI Platform Architecture Design and deploy secure AI infrastructure in Azure GCC High to support enterprise AI capabilities
Establish enterprise access to frontier models, including RAG-enabled knowledge systems and internal chat tools
Build and manage secure API layers that provide controlled access to approved models and AI services
Implement governance controls including RBAC, audit logging, monitoring, and usage tracking, along with cost management and chargeback mechanisms
Collaborate with corporate IT to configure networking, private endpoints, and identity integration required for secure enterprise deployment
Enable engineers and technical teams with controlled environments for coding agents, containers, APIs, web applications, and AI-enabled services
Define and maintain model lifecycle practices including evaluation, approval, deployment, and operational monitoring
Support integration of AI models into analytics pipelines, internal tools, and client-facing solutions
Advise internal teams and both current and prospective clients on AI use case identification, solution design, and implementation approaches aligned to business needs
Provide hands-on technical support for deploying AI-enabled applications, including prototyping, troubleshooting, and performance tuning
Translate AI use cases into practical workflows and operating models to support various client engagements
Ensure architecture and operational practices support CMMC compliance requirements
Architect solutions consistent with Azure GCC High, FedRAMP, and DoD IL requirements
Implement access controls, logging, and data governance policies
Coordinate closely with internal IT on networking, identity, and security
Develop architecture documentation, SOPs, and implementation roadmaps