Cloud Solutions Architect; AI Integration SME

Location: Chantilly, VA

Security Clearance: Active TS or higher [Required]

Job Type: Full-Time

Target Salary Range*: $134,000 - $196,000

This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary

The Cloud Solutions Architect (AI Integration) provides expert leadership in designing and guiding secure, scalable cloud and hybrid technology solutions that support advanced AI capabilities. This role shapes long-term architecture strategies, ensures reliable integration across multiple environments, and guides the secure adoption of modern AI and cloud technologies.

• Lead the design of secure cloud and hybrid architectures supporting AI workloads and enterprise systems.
• Ensure secure, seamless integration across cloud, on-premises, and multi-enclave environments.
• Guide modernization and cloud migration efforts across legacy environments.

• Develop technology roadmaps and standards to guide long-term cloud and AI strategy.
• Evaluate emerging technologies and recommend solutions that enhance performance and security.
• Advise leadership on technical risks, opportunities, and strategic decisions.

• Establish best practices for automation, infrastructure as code (IaC), and Dev Sec Ops  processes.

• Support governance for AI models and data to ensure compliance and responsible use.

• Bachelor’s degree required.

• Minimum of 10 years of experience.
• Experience in cloud architecture, engineering, or related technical leadership roles.
• Experience supporting AI/ML workloads and high-performance computing environments.

• Deep expertise with major cloud platforms and hybrid and on-premises environments.
• Strong understanding of secure architecture frameworks, including zero trust.
• Proficiency with IaC and Dev Sec Ops  toolsets.
• Strong communication skills and the ability to translate technical concepts for leadership.
• Technical capabilities supporting an in-house, custom-developed application.

• Multimodel and multimodal orchestration involving reasoning, perception, memory, and tool execution.
• Temporal, spatial, geographic, quantitative, and computer vision AI implementation.
• Named entity recognition (NER), contextual entity extraction, and entity resolution.
• Multistep retrieval and structured output generation.
• Complex data parsing and streamlined ingestion.
• Detailed source citation and traceable analytic outputs.

• Vector, graph, and multiple database orchestration.
• Automated extract, transform, and load (ETL) pipeline creation, scheduling, and optimization.
• Attribute-based access control (ABAC) at the data layer.
• Data provenance and end-to-end lineage tracking.
• Schema-aligned ingestion and normalization of diverse structured and unstructured data.

• Air-gapped, hybrid, and on-premises operational environments.
• Container orchestration and distributed systems management using Kubernetes.
• Locally hosted GPU servers and hyperconverged nodes.
• Multitenant cross-domain solutions (CDS).

• UI, backend, and middleware engineering using Type Script, React, Python, FastAPI, LanceDB, Lance Graph, PostgreSQL, Temporal.io, Docker, and Kubernetes.
• Integration of reasoning components, retrieval systems, databases, and AI/GenAI services.
• Use of modern programming languages and frameworks.

• Experience in classified or multi-enclave environments.
• Cloud architecture certifications, such as Azure, AWS, or GCP.
• Background with AI/ML platforms, data engineering, or high-performance computing (HPC) systems.
• Advanced degree in a related technical field.