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About This Role
Job Description:
Quevera is seeking a highly skilled AI Platform Engineer with an active TS/SCI clearance with Polygraph to support mission\-critical programs in Northern Virginia. In this role, you will support the development, integration, deployment, and operational maintenance of an enterprise AI platform that delivers secure, cloud\-native capabilities within a classified environment.
Working alongside software engineers and cloud engineering teams, you will troubleshoot and resolve issues related to AI chat functionality, Retrieval\-Augmented Generation (RAG), and enterprise system integrations. You'll develop and maintain integration components, deploy cloud\-native services, and ensure platform components remain secure, compliant, and operationally resilient.
As an AI Platform Engineer, you'll have the opportunity to support cutting\-edge AI capabilities while contributing to the reliability, security, and continuous improvement of enterprise AI solutions supporting critical national security missions.
Work Location:
- Primary Work Location: Customer and AWS facilities in Herndon, Virginia (Northern Virginia).
- Position requires working onsite within a SCIF environment five (5\) days per week.
- Participation in an on\-call rotation is required to support production troubleshooting and system outages.
Job Responsibilities:
- Troubleshoot and debug issues affecting enterprise AI capabilities, including chat functionality, Retrieval\-Augmented Generation (RAG), and integrations with enterprise systems.
- Develop, implement, maintain, and enhance integration components supporting AI platform functionality.
- Maintain version compliance across platform components by applying security patches, bug fixes, feature enhancements, and configuration updates.
- Communicate changes to component versions, configurations, and platform functionality as needed.
- Deploy, monitor, and maintain cloud\-native integration components using AWS services including Lambda, ECS Fargate, DynamoDB, and API Gateway.
- Develop, review, and maintain enterprise application code using Python and TypeScript.
- Collaborate with engineering teams to ensure the reliability, availability, and performance of AI platform services.
- Support ongoing platform operations through troubleshooting, maintenance, and continuous improvement activities.
Minimum Requirements:
- Active TS/SCI clearance with Polygraph required.
- Must already possess an active customer clearance with current customer accounts.
- Must be eligible for crossover to C2E prior to starting.
- Demonstrated proficiency with Python and TypeScript.
- Experience troubleshooting complex software applications within fast\-paced production environments.
- Strong problem\-solving skills with the ability to work independently.
- Willingness to work onsite within a SCIF environment daily, as required.
- Willingness to participate in an on\-call rotation to support production outages and operational issues.
- Mid\- to senior\-level software development experience.
Desired Skills:
- Experience building, deploying, and troubleshooting containerized applications and AWS Lambda functions.
- Experience working with AWS cloud services.
- Experience using AWS CloudFormation and Cloud Development Kit (CDK).
- Experience with Git version control, GitLab, and CI/CD workflows.
- Experience integrating applications with AWS API Gateway.
- Familiarity with cloud monitoring tools.
- Familiarity integrating applications with enterprise services, databases, and customer networks.
- AWS Associate\-level certification or higher.
*Why Join Quevera?*
Award\-Winning Culture
Quevera was recognized as a Top Workplace in the Washington, DC/Baltimore region for 2025, marking our fifth consecutive year receiving this distinction based on employee feedback.
Outstanding Benefits
We invest in our employees and their families through a highly competitive benefits package, including:
- 100% employer\-paid medical coverage (optional plan)
- Competitive options for Medical, Dental and Vision insurance
- Employer\-paid short\-term and long\-term disability coverage
- Employer\-paid life insurance
- $5,000 annually for education, training, certifications, and professional development
- Career advancement through our structured iQTouch Program
- Up to 6% 401(k) match
- Additional 4% profit\-sharing contribution, at company discretion
At Quevera, we believe exceptional people deserve exceptional opportunities. We're more than just a workplace—we're a team of innovators, problem\-solvers, and industry experts committed to delivering mission\-critical solutions while fostering professional growth, collaboration, and technical excellence.
*Quevera is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected veteran status, age or any other characteristic protected by law. \#LI\-AA1*
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 Quevera, 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 $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.
Quevera AI Hiring
Quevera has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Herndon, VA, US.
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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