Senior Cloud Architect Lead with AI Cloud

SAIC•Flexwork, GA

3d•Remote

About The Position

SAIC is seeking a Senior Cloud Architect with AI Cloud expertise to make a difference for national security by joining a team of dedicated IT professionals who will sustain, modernize, and transform the enterprise IT capabilities for the Defense Counterintelligence and Security Agency (DCSA). The Air Force, Space & Intel Business Group (AFSI) of SAIC is seeking a Senior Cloud Architect Lead to support a transformational infrastructure program for DCSA. SAIC is proud to be supporting DCSA in safeguarding our nation’s information. DCSA is the designated oversight authority on the accreditation of classified facilities, information systems, and the insider threat program. This involves security oversight of more than 10,000 companies and approximately 13,000 facilities involved in classified work throughout the DoD and 31 Federal agencies. Specifically, on the DCSA One IT program, SAIC will provide an enterprise IT solution that delivers highly secure and adaptable IT infrastructure, provide customer support, and cutting-edge technologies that support operations and advance the DCSA mission under a single IT environment (i.e., One IT). This position is remote with limited travel.

Requirements

Serve as a cloud architecture leader, providing guidance and mentorship to team members, while supporting a DoD mission program.
Design and deliver secure, compliant workloads within a platform-managed hub-and-spoke environment across AWS GovCloud and Azure Government, integrating advancements in AI Cloud capabilities for enhanced mission impact.
Architect and deliver secure, scalable AWS‑centric solutions (with multi‑cloud fluency across Azure Government/GCP) as a spoke workload team operating inside a platform‑managed hub‑and‑spoke environment.
Be well‑versed in platform management constructs (network hub, identity, operations, DevOps, shared services) to facilitate design discussions and articulate workload requirements to platform owners/providers for both traditional cloud services and emerging AI-enabled services.
Operate as a liaison between mission teams, leadership, and platform providers.
Ensure workload strategies, including AI/ML-related initiatives, align with programmatic, operational, and compliance goals.
Translate complex requirements, including both traditional infrastructure needs and innovative AI-based workloads, into practical architectures while balancing compliance with DoD operational constraints (Cloud Computing SRG impact levels, RMF/ATO, DISA STIGs).
Mentor and manage team members involved in workload architecture and cloud deployment to ensure technical proficiency, adherence to compliance requirements, and timely delivery of mission objectives.
Foster a collaborative team environment, driving alignment on priorities and ensuring clear communication.
Act as the primary technical point of contact for workload-related activities, providing direction to the team while coordinating with external stakeholders, including platform owners, vendor teams, and mission partners.
Define and communicate workload requirements for routing, firewall/inspection, DNS, identity trust, logging/telemetry, secrets, and egress, and AI infrastructure, packaged as intake/change requests to the platform team with clear technical specifications and risk/treatment rationales.
Manage cross-functional teams and discussions, ensuring alignment between workload needs and platform provisioning.
Clarify roles and responsibilities for components like TGW attachments, VPCs, AI inference endpoints, and secured data pipelines supporting AI workflows.
Drive the creation of workload reference architectures and IaC templates (Terraform/CloudFormation/Bicep/CDK) while expanding these assets to support AI/ML pipelines (training, inference, monitoring) under RL and mission compliance guardrails.
Lead the team in Implementing secure network zoning and service exposure (PrivateLink/VPC endpoints, ALB/NLB, WAF) ensuring both traditional and AI-based services.
Design end-to-end AI/ML solutions by incorporating CI/CD pipelines with security/compliance gates, model versioning, artifact storage policies, and data lineage tracing that comply with RMF and logging/monitoring requirements (CloudTrail/Config/Security Hub, Azure Log Analytics/Sentinel).
Map workload data and mission needs to SRG IL2–IL6 and engineer control implementations that leverage platform inheritance where available; drive RMF documentation, STIG hardening/SCAP automation, and ATO/IATT artifacts for the workload.
Provide team guidance on applying Zero Trust principles, including identity‑centric access, micro‑segmentation, and DevSecOps, ensuring alignment with DoD mission cloud practices.
Lead collaboration efforts with external vendors and industry solution providers to evaluate AI Cloud/COTS/ISV solutions for mission-specific use cases while ensuring DoD compliance.
Facilitate engineering design reviews, ensuring the ability to document trade-offs, residual risks, and mitigation plans in alignment with DoD guidelines.
Define and manage workload resilience strategies, including Multi‑AZ/Region configurations, backups, and failover mechanisms within impact level boundaries; document DR strategies and exercise runbooks compatible with platform‑managed services.
Guide team members in implementing and monitoring FinOps practices for cost optimization in managing both cloud compute resources and AI/ML workloads (e.g., cost-efficiency of training jobs, instance rightsizing, serverless inference optimization).

Nice To Haves

AI Cloud capabilities
Multi-cloud fluency across Azure Government/GCP
Platform management constructs (network hub, identity, operations, DevOps, shared services)
AI/ML-related initiatives
Cloud Computing SRG impact levels, RMF/ATO, DISA STIGs
IaC templates (Terraform/CloudFormation/Bicep/CDK)
AI/ML pipelines (training, inference, monitoring)
Zero Trust principles
DevSecOps
AI Cloud/COTS/ISV solutions
FinOps practices

Responsibilities

Serve as a cloud architecture leader, providing guidance and mentorship to team members, while supporting a DoD mission program. Design and deliver secure, compliant workloads within a platform-managed hub-and-spoke environment across AWS GovCloud and Azure Government, integrating advancements in AI Cloud capabilities for enhanced mission impact.
Architect and deliver secure, scalable AWS‑centric solutions (with multi‑cloud fluency across Azure Government/GCP) as a spoke workload team operating inside a platform‑managed hub‑and‑spoke environment.
Be well‑versed in platform management constructs (network hub, identity, operations, DevOps, shared services) to facilitate design discussions and articulate workload requirements to platform owners/providers for both traditional cloud services and emerging AI-enabled services.
Operate as a liaison between mission teams, leadership, and platform providers. Ensure workload strategies, including AI/ML-related initiatives, align with programmatic, operational, and compliance goals.
Translate complex requirements, including both traditional infrastructure needs and innovative AI-based workloads, into practical architectures while balancing compliance with DoD operational constraints (Cloud Computing SRG impact levels, RMF/ATO, DISA STIGs).
Mentor and manage team members involved in workload architecture and cloud deployment to ensure technical proficiency, adherence to compliance requirements, and timely delivery of mission objectives.
Foster a collaborative team environment, driving alignment on priorities and ensuring clear communication.
Act as the primary technical point of contact for workload-related activities, providing direction to the team while coordinating with external stakeholders, including platform owners, vendor teams, and mission partners.
Define and communicate workload requirements for routing, firewall/inspection, DNS, identity trust, logging/telemetry, secrets, and egress, and AI infrastructure, packaged as intake/change requests to the platform team with clear technical specifications and risk/treatment rationales.
Manage cross-functional teams and discussions, ensuring alignment between workload needs and platform provisioning. Clarify roles and responsibilities for components like TGW attachments, VPCs, AI inference endpoints, and secured data pipelines supporting AI workflows.
Drive the creation of workload reference architectures and IaC templates (Terraform/CloudFormation/Bicep/CDK) while expanding these assets to support AI/ML pipelines (training, inference, monitoring) under RL and mission compliance guardrails.
Lead the team in Implementing secure network zoning and service exposure (PrivateLink/VPC endpoints, ALB/NLB, WAF) ensuring both traditional and AI-based services.
Design end-to-end AI/ML solutions by incorporating CI/CD pipelines with security/compliance gates, model versioning, artifact storage policies, and data lineage tracing that comply with RMF and logging/monitoring requirements (CloudTrail/Config/Security Hub, Azure Log Analytics/Sentinel).
Map workload data and mission needs to SRG IL2–IL6 and engineer control implementations that leverage platform inheritance where available; drive RMF documentation, STIG hardening/SCAP automation, and ATO/IATT artifacts for the workload.
Provide team guidance on applying Zero Trust principles, including identity‑centric access, micro‑segmentation, and DevSecOps, ensuring alignment with DoD mission cloud practices.
Lead collaboration efforts with external vendors and industry solution providers to evaluate AI Cloud/COTS/ISV solutions for mission-specific use cases while ensuring DoD compliance.
Facilitate engineering design reviews, ensuring the ability to document trade-offs, residual risks, and mitigation plans in alignment with DoD guidelines.
Define and manage workload resilience strategies, including Multi‑AZ/Region configurations, backups, and failover mechanisms within impact level boundaries; document DR strategies and exercise runbooks compatible with platform‑managed services.
Guide team members in implementing and monitoring FinOps practices for cost optimization in managing both cloud compute resources and AI/ML workloads (e.g., cost-efficiency of training jobs, instance rightsizing, serverless inference optimization).