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Title:
Ai Platform ArchitectProgram Summary
KBR’s Missile, Aviation, and Ground Systems (MAGS) division delivers mission engineering solutions for critical U.S. Army programs, specializing in aviation and ground systems, integrated air and missile defense, and threat and target systems. As a trusted partner of the U.S. Department of Defense, MAGS provides innovative, technology\-driven solutions to enhance national security. With a global presence and a strong ethical framework, KBR ensures secure, effective, and mission\-ready capabilities worldwide.
Job Summary
As an AI Platform Architect, you will play a critical role in designing, building, and sustaining a secure, on\-premises AI platform within a classified environment. This unique opportunity places you at the forefront of enabling advanced AI capabilities for software developers, analysts, and program managers supporting mission\-critical initiatives.
In this role, you will lead the deployment of a classified High\-Performance Computing (HPC) environment to create scalable, secure, and resilient AI infrastructure. You will work closely with Huntsville Site Program Management and Technical Management leads in constructing and deploying all necessary infrastructure to host on\-premises AI models and DevSecOps resources utilizing a high\-performance computing cluster. This includes the architecting, deployment, and sustainment of HPC operations, networking, on\-premises cloud platforms, classified data partitioning and segregation, AI model hosting and lifecycle management, and multi\-tenant DevSecOps ecosystems. In partnership with Program, Technical, and Information Assurance (IA) stakeholders, you will ensure compliance with security requirements and lead efforts to achieve and maintain Authority to Operate (ATO) accreditation for mission\-critical AI capabilities.
Key Responsibilities
- Work across multiple technology stacks and software tools to build, deploy and maintain on\-premises AI multi\-tenant hosting.
- Work across multiple technology stacks and software tools to build, deploy and maintain on\-premises DevSecOps multi\-tenant hosting.
- Lead the architecture, deployment, and sustainment of networking infrastructure supporting classified AI and DevSecOps environments.
- Utilize existing and future High Performance Computing Hardware to determine appropriate resource utilization for multi\-tenant AI models and DevSecOps infrastructure
- Interface with Program Managers and Technical Experts to implement and maintain appropriate AI data segregation across classified programs.
- Interface with Information Assurance to ensure security compliance to achieve and maintain ATO accreditation.
- Serve as the lead classified DevSecOps interface between unclassified software development and classified development.
- Serve as the technical authority for classified AI platform architecture, infrastructure modernization, and platform scalability initiatives.
- Stay current with software industry best practices, including use of AI in software development, cloud technologies, and evolving security threats to recommend innovative solutions.
Basic Qualifications:
- U.S. Citizenship is required.
- Active/current Secret Security Clearance is required.
- Possess a Bachelor of Science degree in Computer Science or other STEM related fields (Engineering, Physics, Mathematics, CIS, etc.) with 10\+ years of experience or a master’s degree in computer science or related STEM fields with 5\+ years of experience.
- Experience with DevSecOps infrastructure and tools
- Experience with on\-premises AI infrastructure and tools
- Experience with handling vector data from structured and unstructured sources (Microsoft products, binary files, software development files, databases)
- Deep knowledge about mechanics of building AI platform as a service, agent to agent orchestration, agentic layer integration, AI UX layer and conversational AI models.
- Linux knowledge is a must
Preferred Qualifications:
- Experience deploying and scaling LLM inference platforms using vLLM, NVIDIA NIM or similar technologies.
- Strong architecture skills in Distributed Systems, Data Engineering, Cloud Architectures and Platform Engineering.
- Experience in providing clear guidance on designing secure and regulated AI platforms at local laboratory scale.
- Experience designing RMF compliant systems (e.g. RMF, NIST 800\-53, NIST AI RMF, ICD 503, DISA STIGs, and DoD Zero Trust requirements)
- Demonstrated experience with NVIDIA GPU architectures (H100, H200, A100, RTX, DGX platforms, etc.) and distributed computing environments
- Experience with GPU resource scheduling and optimization for multi\-tenant AI models
- Experience deploying and operating vector databases including OpenSearch, Elasticsearch, or equivalent for data segregation and information flow enforcement.
- Experience implementing MLOps platforms including model lifecycle management, evaluation pipelines, model registries, and continuous AI delivery practices.
- Experience with data governance, metadata systems, semantic layers and knowledge graphs.
- Experience deploying and scaling container orchestration tools
- Experience deploying and scaling security scanning tools like Trivy, SonarQube, Checkmarx.
- Experience with source code management tools like Git, AzureDevOps, GitHub
- Experience with CI/CD Tools
- Experience deploying and scaling Identity \& Access Management (IAM) with role\-based and attribute\-based access for local laboratory scale.
Belong, Connect and Grow at KBR
At KBR, we are passionate about our people and our Zero Harm culture. These inform all that we do and are at the heart of our commitment to, and ongoing journey toward being a People First company. That commitment is central to our team of team’s philosophy and fosters an environment where everyone can Belong, Connect and Grow. We Deliver – Together.
KBR is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity or expression, age, national origin, veteran status, genetic information, union status and/or beliefs, or any other characteristic protected by federal, state, or local law.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At KBR, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
KBR AI Hiring
KBR has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Huntsville, AL, US, Chantilly, VA, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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