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About This Role
Job ID: 2613496
Location: Remote Work, VA, US
Date Posted: 2026\-06\-09
Category: Information Technology
Subcategory: Enterprise Architect
Schedule: Full\-Time
Shift: Day Job
Travel: Yes \- 10% of the time
Minimum Clearance Required: Top\_Secret
Clearance Level Must Be Able to Obtain: None
Potential for Remote Work: ORA\_REMOTE
Description
SAIC is seeking Senior Cloud Architect with AI Cloud. 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.
Description
- 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).
Key Responsibilities
- Leadership \& Team Management
+ 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.
- Requirements \& Coordination with Platform Providers
+ 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.
- Architecture \& Delivery (Spoke Workloads)
+ 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).
- DoD Compliance \& Security Engineering (Within Workload Scope)
+ 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.
- Vendor/ISV Collaboration \& Technical Assessments
+ 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.
- Reliability, Resilience \& Cost Management
+ 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).
Qualifications
10\+ years in cloud architecture/engineering with deep hands‑on AWS experience; proven delivery of secure workloads in AWS GovCloud (US) and/or Azure Government supporting DoD missions within a platform‑managed hub‑and‑spoke environment.
- Government program experience and proven team leadership skills, including managing, mentoring, and guiding technical teams toward achieving project and operational goals.
- Demonstrated ability to translate system requirements into technical solutions and to negotiate workload needs with platform owners/providers (networking, identity, security, logging/monitoring, DevOps).
- Strong grasp of DoD SRG, RMF (NIST SP 800‑53/53B), DISA STIGs, ATO/IATT processes, and continuous monitoring/POA\&M practices; experience leading teams in the implementation of these practices into workload designs.
Certifications (Required)
- CompTIA Security\+ (CE).
- At least one Professional/Expert‑level cloud certification (Associate/Foundation does not meet the requirement):
+ AWS Certified Solutions Architect – Professional (preferred), AWS DevOps Engineer – Professional, or another AWS Professional.
+ Microsoft Certified: Azure Solutions Architect Expert.
+ Google Professional Cloud Architect.
+ Relevant specialty certs (e.g., Advanced Networking, Security) are a plus when paired with a Professional/Expert certification.
Preferred Skills \& Tools
- Leadership Skills: Experience managing teams, driving cross\-functional collaboration, and mentoring junior members to grow technical, compliance, and operational expertise.
- AI \& ML Integration: Experience with deploying AI/ML pipelines using SageMaker, TensorFlow, MLflow, Kubernetes (for serving/training ML models), or equivalents on AWS, Azure ML, or GCP Vertex AI.
- IaC \& automation: Terraform, CloudFormation, CDK, Bicep; configuration as code (Ansible/PowerShell/Bash).
- CI/CD: GitLab/GitHub Actions/Azure DevOps with policy gates and security scanning aligned to platform controls.
- Networking: Transit Gateway, VPC/VNet segmentation, PrivateLink/VPC endpoints, firewall policies; ability to specify correct TGW attachment parameters and route associations while respecting platform ownership.
- Security services \& monitoring: AWS Security Hub/GuardDuty/Config/KMS, Azure Sentinel/Defender; integration to centralized logging/telemetry required by the platform.
- Documentation \& compliance: Author SSP sections, control inheritance matrices, STIG baselines, and continuous monitoring playbooks for workloads.
Education/Clearance Requirements:
- Bachelor's degree with 10\+ years of hands\-on and leadership experience as a Cloud Architect specializing in AWS, Azure, and/or AI Cloud solutions; master’s degree with 7\+ years of experience.
- Ability to obtain DoD 8570 certification if not already held.
- US Citizenship required.
- Currently holding an active Top Secret Security Clearance.
Target salary range: $120,001 \- $160,000\. The estimate displayed represents the typical salary range for this position based on experience and other factors.
Salary Context
This $120K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At SAIC, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 23% below the category median. Disclosed range: $120K to $160K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
SAIC AI Hiring
SAIC has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Chantilly, VA, US, Washington, DC, US, VA, US. Compensation range: $160K - $200K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.
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