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About This Role
Job Title: AI Platform Architect Job Category: Information Technology Time Type: Full time Minimum Clearance Required to Start: Secret Employee Type: Regular Percentage of Travel Required: Up to 10% Type of Travel: Local
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The Opportunity:
We are seeking an experienced AI Platform Architect to lead the design, implementation, and operation of an enterprise AI workflow management platform supporting the Department of the Air Force. This role will architect solutions in Azure IL-5 cloud environments, integrate with ServiceNow, and enable both technical and non-technical users to develop AI-driven workflows that enhance mission-critical government services.
You will be responsible for establishing a scalable, secure AI platform within our Azure cloud enclave that connects to DISA-compliant networks and ServiceNow instances. You'll evaluate and integrate multiple AI tools including third-party agentic platforms, Microsoft AI Foundry, and additional systems to be determined, while helping to build a team capable of delivering production-ready AI services to government customers.
Responsibilities:
Platform Architecture & Engineering
- Design and implement cloud infrastructure for AI workflow management platforms in Azure IL-5 environments
- Architect containerized solutions using Azure Kubernetes Service (AKS) or similar with appropriate security controls for IL-5 compliance
- Design data architecture including PostgreSQL database solutions optimized for AI workloads
- Establish integration patterns for Azure OpenAI services, ServiceNow APIs, and cross-boundary connections via DISA BCAP
- Evaluate and integrate AI development tools into unified platform architecture
- Design and implement API gateways, authentication/authorization frameworks, and service mesh architectures
- Establish RAG (Retrieval-Augmented Generation) pipelines, vector databases, and embedding strategies
- Create reference architectures for agentic workflows, document generation, and ML model deployment.
- Operations & Platform Health.
- Establish operational monitoring, logging, and alerting procedures for platform health and AI service performance
- Implement MLOps practices including model versioning, A/B testing, and automated retraining pipelines
- Monitor AI model performance, drift detection, and data quality across production workflows
- Design disaster recovery and business continuity plans for mission-critical AI services
- Work with ServiceNow and Azure cloud engineers to troubleshoot complex integration issues across Azure, ServiceNow, and government networks
- Ensure platform scalability and cost optimization within government budget constraints
Team Leadership & Development
- Help build and lead a team of AI/ML engineers, providing direction toward identified objectives and technical oversight
- Participate in technical screening and onboarding of AI/ML talent
- Establish engineering standards, best practices, and documentation for AI development
- Foster collaboration between AI engineers, ServiceNow developers, and government stakeholders
- Conduct design reviews and provide architectural guidance to engineering teams
- Promote a culture of innovation, experimentation, and continuous improvement
Government & Compliance Navigation
- + Support cybersecurity teammates in navigating Department of the Air Force regulations, Authority to Operate (ATO) requirements, and compliance frameworks
- + Work within DISA Security Technical Implementation Guides (STIGs) and Impact Level 5 requirements
- + Coordinate with cybersecurity security teams for accreditation and continuous monitoring
- + Understand and implement controls for CUI (Controlled Unclassified Information) and data classification requirements
Use Case Development & QA
- + Partner with government customers to understand mission requirements and translate into technical solutions
- - Evaluate LLM capabilities (GPT-5+, IL-5-approved alternatives) for specific use cases including RAG, document generation, and agentic workflows
- + Respond to real-world government use cases with prototype solutions and proof-of-concepts
- + Establish QA frameworks for AI services including model testing, integration testing, and user acceptance testing
- + Oversee quality assurance processes or manage QA specialists ensuring AI output accuracy and reliability
- + Define success metrics and evaluation criteria for AI-driven workflows.
Qualifications:
Required:
- + US Citizenship required
- + Must meet eligibility requirements for access to classified information and be successfully in obtain to a Department of Defense (DoD) Secret Clearance and pass all relevant background checks for a security clearance.
- + Active DoD clearance preferred but not required.
- + 15+ Years of relevant experience (Bachelor’s Degree in relevant field may be substituted for 5 years of relevant experience).
- + Experience converting real-life use cases into deployable AI/ML solutions
- + Strong expertise in Azure cloud services (AI Foundry, DevOps, PostgreSQL, API Management, networking)
- + Proven experience with containerization (Docker, Kubernetes), microservices architectures, and VMs
- + Deep knowledge of LLM technologies, RAG architectures, vector databases, and prompt engineering
- + Hands-on experience with AI/ML frameworks (LangChain, LlamaIndex, TensorFlow, PyTorch, scikit-learn, other)
- + Strong understanding of API design, integration patterns, and enterprise service buses
- + Experience with MLOps tools and practices (model versioning, monitoring, CI/CD for ML)
- + Excellent communication skills with ability to translate technical concepts for non-technical stakeholders
- + Experience working directly with customers and understanding mission requirements
- + Proficiency in Python and/or other languages commonly used in AI development
- + Understanding of workflow orchestration tools and business process automation
Desired:
- + Active Secret clearance (Top Secret eligibility preferred)
- + Experience working in government cloud environments (Azure Government, AWS GovCloud, or similar)
- + Familiarity with DoD compliance frameworks (IL-4/IL-5, FedRAMP, NIST 800-53, STIGs)
- + Understanding of DISA network architectures and cross-boundary connection requirements
- + Experience with Authority to Operate (ATO) processes and security accreditation
- + Experience with ServiceNow platform (integration, Flow Designer, or development preferred).
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What You Can Expect:
A culture of integrity.
At CACI, we place character and innovation at the center of everything we do. As a valued team member, you’ll be part of a high-performing group dedicated to our customer’s missions and driven by a higher purpose – to ensure the safety of our nation.
An environment of trust.
CACI values the unique contributions that every employee brings to our company and our customers - every day. You’ll have the autonomy to take the time you need through a unique flexible time off benefit and have access to robust learning resources to make your ambitions a reality.
A focus on continuous growth.
Together, we will advance our nation's most critical missions, build on our lengthy track record of business success, and find opportunities to break new ground — in your career and in our legacy.
Pay Range :
There are a host of factors that can influence final salary including, but not limited to, geographic location, Federal Government contract labor categories and contract wage rates, relevant prior work experience, specific skills and competencies, education, and certifications. Our employees value the flexibility at CACI that allows them to balance quality work and their personal lives. We offer competitive compensation, benefits and learning and development opportunities. Our broad and competitive mix of benefits options is designed to support and protect employees and their families. At CACI, you will receive comprehensive benefits such as; healthcare, wellness, financial, retirement, family support, continuing education, and time off benefits.
Since this position can be worked in more than one location, the range shown is the national average for the position.
The proposed salary range for this position is:
$114,600-$252,100 CACI is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, age, national origin, disability, status as a protected veteran, or any other protected characteristic.
Salary Context
This $114K-$252K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CACI International, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($183K) sits 14% below the category median. Disclosed range: $114K to $252K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
CACI International AI Hiring
CACI International has 10 open AI roles right now. They're hiring across AI/ML Engineer, Prompt Engineer, Data Scientist. Positions span King of Prussia, PA, US, Denver, CO, US, Ashburn, VA, US. Compensation range: $75K - $252K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
Frequently Asked Questions
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