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About This Role
540 is seeking a Senior Solutions Architect (AI/ML \& Cloud) to support a high\-visibility Department of War initiative within the Chief Digital and Artificial Intelligence Office (CDAO) Test \& Evaluation (T\&E) organization. This role will help advance next\-generation capabilities across cloud\-native platforms, AI/ML systems, DevSecOps practices, and data\-driven mission applications that support critical test and evaluation efforts.
As a trusted technical leader, you will partner with government stakeholders, engineers, and architects to design and deliver scalable, secure, and resilient solutions that accelerate innovation and enable the adoption of emerging technologies across complex defense environments.
Location: Arlington, VA with hybrid work at the Pentagon
Citizenship \& Clearance Requirement: Per client requirements, candidates must be U.S. Citizens with an active DoW Top Secret clearance
Education Requirement: Bachelor's degree in Computer Science, Engineering, Information Systems, Mathematics, or related
540 Internal Thrive Level: Senior Solutions Architect
WHY 540?
540 is a forward\-thinking company that the government turns to in order to \#getshitdone. We don't just talk about innovation – we deliver it. We break down barriers, build impactful technology, and solve mission\-critical problems.
HOW YOU'LL DRIVE IMPACT
- Serve as a hands\-on technical leader, actively designing, prototyping, and building cloud\- and AI\-enabled solutions while providing architectural guidance and mentorship to engineering teams
- Lead the architecture, design, and delivery of cloud\-native solutions supporting mission\-critical modernization initiatives across federal and defense environments
- Provide technical leadership and strategic direction to engineering teams while helping expand technical capabilities, project scope, and customer impact
- Advise and lead teams on integrating AI/ML capabilities, MLOps processes, and advanced analytics solutions into enterprise applications and mission workflows
- Support business growth activities through technical strategy, solution development, proposal support, and customer engagement
- Partner with customers, technical teams, and stakeholders to define technology roadmaps, solution architectures, and implementation strategies aligned to mission objectives
- Guide cloud migration and modernization efforts, helping organizations adopt scalable, resilient, and secure AWS\-based platforms
- Architect and champion DevSecOps practices, CI/CD automation, Infrastructure as Code (IaC), and modern software delivery methodologies to improve speed, quality, and reliability
- Design secure, cloud\-native application and data architectures leveraging containerized platforms, Kubernetes, and modern engineering best practices
- Collaborate with software, data, and platform engineers to deliver scalable solutions that support data\-driven decision\-making and operational effectiveness
- Establish engineering standards for automated testing, quality assurance, system reliability, and continuous improvement across development teams
- Apply Zero Trust principles, cloud security best practices, and federal cybersecurity requirements throughout the solution lifecycle
REQUIRED SKILLS \& EXPERIENCE
- 10\+ years of experience designing, developing, and implementing enterprise technology solutions, including at least 3 years of experience supporting federal, defense, or national security environments
- Experience designing, developing, and integrating AI/ML solutions, MLOps capabilities, data platforms, and advanced analytics solutions
- Demonstrated experience leading and growing technical teams, with the ability to expand both project scope and delivery capabilities
- Extensive experience leading Amazon Web Services (AWS) cloud migration, modernization, and cloud\-native transformation efforts
- Experience building and deploying AI/ML solutions using AWS services such as Bedrock, SageMaker, or equivalent platforms
- Experience supporting data engineering, data science, and analytics platforms
- Demonstrated experience implementing and managing DevSecOps methodologies, CI/CD pipelines, Infrastructure as Code (Terraform, CloudFormation), and automated deployment frameworks
- Experience with Kubernetes and containerized platforms
- Proven experience designing and implementing automated testing frameworks supporting functional, integration, regression, performance, and security testing
- Experience incorporating automated testing, quality assurance, and reliability practices within modern software delivery pipelines
- Experience developing technical architectures, solution designs, and modernization roadmaps
- Experience leading technical discussions with executive stakeholders, customers, and mission partners
- Knowledge of cloud security architectures, Zero Trust principles, and federal cybersecurity requirements
- Strong understanding of Agile software development methodologies and software engineering best practices
- Strong communication, stakeholder engagement, problem\-solving, and technical leadership skills
- AWS Professional\-level certification (Solutions Architect Professional, DevOps Engineer Professional, or equivalent), or the ability to obtain within 30 days of employment
- CompTIA Security\+ certification, or the ability to obtain within 30 days of employment
NICE TO HAVE
- Experience supporting highly secure federal cloud environments and Authority to Operate (ATO) initiatives
- Experience with Amazon EKS and large\-scale Kubernetes platform operations
- Experience with DevSecOps toolchains, including Jenkins, GitLab CI/CD, GitHub Actions, or Azure DevOps
- Experience with automated testing tools and test automation frameworks
- Experience implementing AI/ML deployment frameworks in production environments
- Familiarity with large\-scale testing, evaluation, modeling, simulation, or mission engineering environments
BENEFITS \& PERKS
- Flexible PTO \+ all Federal holidays off
- Health, dental and vision insurance plans
- Flexible Spending Account (FSA)
- 401k with employer match
- Company\-sponsored life insurance, short\- and long\-term disability
- Professional development (training, certifications, conferences)
- Paid cloud developer accounts
- Referral bonuses
- HQ office perks (parking / metro reimbursement, nitro coffee \& lunches)
- Annual social events (540 Week, hackathon, charity golf tournament, etc.)
- Access to 540's Washington Capitals \& Nationals tickets
EQUAL EMPLOYMENT OPPORTUNITY (EEO)
540's policy is to provide equal employment opportunity to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
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 540, 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.
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.
540 AI Hiring
540 has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in Arlington, VA, US.
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.
Frequently Asked Questions
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