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About This Role
CLEARANCE REQUIRED: TS/SCI with CI Poly
This job is located at: McClean, VA
Job description:
1\. AI Tool Evaluation \& Configuration
Assess approved AI/ML tools currently available on the customer network and evaluate their operational readiness, configuration gaps, and underutilization.
Configure, optimize, and integrate approved tools into existing analytic and targeting workflows without introducing unapproved capabilities or triggering additional review board requirements.
Develop mission\-specific use\-case configurations that align tool functionality to analyst tasks — entity triage, credibility scoring, pattern correlation, document production, and RFI processing.
Maintain tool performance baselines and identify configuration adjustments that improve output accuracy, speed, and analyst adoption.
2\. Workflow Analysis \& Process Redesign
Map current\-state analytic and operational workflows to identify where approved AI tools can eliminate manual bottlenecks, reduce redundant data entry, and compress cycle times.
Design optimized future\-state workflows that embed AI tool touchpoints at the highest\-friction points in the intelligence production and targeting cycle.
Develop before/after process documentation with measurable performance targets tied directly to mission outcomes.
Maintain SOPs and workflow guides that reflect the integrated AI\-enabled process architecture.
3\. Prompt Engineering \& Tool Enablement
Build mission\-specific prompt libraries, Boolean\-to\-AI logic translation guides, and structured templates that make approved tools immediately usable by analysts without requiring technical expertise.
Develop a Document Support Playbook Suite covering draft assist, tradecraft review, source synthesis, consistency checking, and classification review workflows.
Ensure all prompt engineering products are tool\-agnostic and adaptable to any customer\-approved platform upgrade or replacement.
4\. Performance Measurement \& Continuous Improvement
Establish KPIs tracking AI tool utilization rates, analyst productivity gains, cycle time reductions, and product quality improvements.
Provide leadership with data\-driven evidence supporting review board decisions to expand AI tool access or activate additional use cases.
Apply Lean Six Sigma and continuous improvement methodologies to iteratively refine AI\-integrated workflows based on operational feedback.
5\. Stakeholder Collaboration \& Change Management
Work directly with analysts, targeters, mission leads, and IT teams to drive adoption of AI\-integrated workflows through hands\-on demonstration, embedded support, and structured enablement.
Develop transition plans and training materials that ensure smooth integration of AI tools into daily mission operations with zero workflow disruption.
Serve as the operational bridge between the technical AI/ML engineering team, the analytic workforce, and program leadership.
Required Qualifications:
Education: Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field.
Experience: 10\+ years of experience in AI/ML tool deployment, systems integration, or business process engineering; at least 5 years supporting IC, DoD, or Federal law enforcement analytic environments.
Technical Skills: Proficiency in AI/ML tool configuration, prompt engineering, workflow modeling (BPMN), and data pipeline management; experience with IC\-approved analytic platforms and multi\-classification network environments.
Methodologies: Working knowledge of Lean Six Sigma, Agile, and continuous improvement frameworks applied to operational or intelligence environments.
Soft Skills: Strong analytical thinking, clear written and verbal communication, and the ability to translate technical AI capability into practical mission value for non\-technical analysts.
Clearance: Active TS/SCI with CI Polygraph required.
Why work for QSL?
Our founders, Mel Wick and Bill Cronin, retired from storied careers in the Special Operations Forces (SOF) Community. Like many Americans and military veterans, they felt a strong desire to support the nation’s response to the 9/11/2001 terrorist attacks on the World Trade Center and the Pentagon in any way they could. They established QSL to do just that, Stay in the Fight! QSL is built on a SOF culture, emphasizing selfless\-service and teamwork. Our employees work to ensure that warfighters have every possible resource and all necessary support to safely accomplish their missions in defense of our nation.
QSL's Benefit Package
Because we believe our employees are our most valuable asset, offering a competitive comprehensive compensation package is very important to us. It is the goal of QSL to attract and retain the highest level of experience and technical talent necessary for successful performance. In order to accomplish this, we feel that it is necessary to provide satisfying work, an excellent work environment, and we continually monitor the marketplace to ensure that our total compensation/benefit package remains competitive.
Listed below are some of our standard benefits. We combine all traditional paid time off (Federal holidays, sick time, leave time personal days, jury duty, bereavement, etc.) into one category which allows employees flexibility in how they use their leave time and enables them to better balance their career with their personal needs.
- Combined Paid Time Off (PTO)
- Medical, Dental, Life Insurance
- Disability (Short\-Term and Long\-Term)
- Vision Insurance (CONUS\-based employees)
- Flexible Spending Account (FSA)
- 401(k) Retirement Plan
- Employee Referral Bonus Program
- Employee Discount Programs
- Critical Illness and Accident Insurance
- Employee Assistance Program
We are an Equal Opportunity Employer. We do not and will not discriminate in employment and personnel practices based on race, sex, age, disability, veteran status, religion, national origin or any other basis prohibited by applicable law. Hiring, transferring, and promotion practices are performed without regard to the above listed items. EEO/AAP, M, F, V, D.
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 Quick Services LLC, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
Quick Services LLC AI Hiring
Quick Services LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sterling, 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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