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Network Engineer – Level 3
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Employment Type: Full\-Time
Clearance: Active TS/SCI FS Poly
Position Overview
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We are seeking an experienced Network Engineer – Level 3 to support the planning, design, development, implementation, operation, and technical support of complex multi\-platform and multi\-system network environments.
This role will focus on maintaining secure, high\-performance enterprise networks, diagnosing network performance issues, implementing improvements, and providing technical leadership for network operations and security. The ideal candidate will bring deep hands\-on experience with Cisco routing, switching, firewalls, VPNs, intrusion detection/prevention systems, and enterprise\-level backbone networks.
Key Responsibilities
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- Plan, design, develop, implement, and support secure enterprise network environments.
- Install and configure Cisco 7600 Series routers and Cisco 6500 Series switches.
- Maintain, monitor, troubleshoot, diagnose, and resolve issues involving:
+ Cisco ASA firewalls
+ Cisco Intrusion Detection/Prevention Systems
+ Cisco VPN technologies
+ Cisco 3750 Series switches
+ Cisco network management applications
- Identify network performance limitations and implement solutions to improve availability, reliability, and performance.
- Develop network security requirements and policies and translate those requirements into device\-specific configurations.
- Design and implement enterprise\-level ISP backbone networks.
- Operate and manage networks within secure and controlled environments.
- Provide technical direction for the operation, configuration, maintenance, and troubleshooting of network devices.
- Analyze complex technical issues and develop effective resolution strategies.
- Lead or direct small technical teams in identifying and resolving complex network problems.
- Maintain network documentation, configuration standards, and operational procedures.
- Support secure network architecture and ensure network configurations align with organizational security requirements.
Required Qualifications
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- 10\+ years of experience in network design, including:
+ Routing
+ Switching
+ Network security
- Demonstrated experience installing and configuring Cisco enterprise networking equipment.
- Strong experience supporting Cisco routing, switching, firewall, VPN, and intrusion detection/prevention technologies.
- Experience designing and implementing enterprise\-scale network architectures.
- Experience developing and implementing network security requirements and policies.
- Demonstrated ability to diagnose and resolve complex network performance and connectivity issues.
- Experience providing technical leadership or direction to small engineering teams.
- Strong understanding of secure network operations and configuration management.
Required Certification
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- Cisco Certified Network Associate (CCNA) certification is required.
Technical Environment
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- Cisco 7600 Series Routers
- Cisco 6500 Series Switches
- Cisco 3750 Series Switches
- Cisco ASA Firewalls
- Cisco IDS/IPS
- Cisco VPN
- Cisco Network Management Applications
- Enterprise Routing \& Switching
- Network Security
- ISP Backbone Networks
- Secure Network Environments
Preferred Attributes
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- Strong analytical and troubleshooting skills.
- Ability to work independently on complex technical issues.
- Strong written and verbal communication skills.
- Experience mentoring or leading junior network engineers.
- Ability to translate technical and security requirements into practical network configurations.
- Experience supporting mission\-critical or highly secure enterprise networks.
- Benefits
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Our client offers a comprehensive, flexible benefits package supporting financial security, health, and work\-life balance.
Retirement
- 15% automatic company 401(k) contribution (3% safe harbor \+ 12% discretionary profit sharing)
- No vesting – 100% immediately vested
- Pre\-tax and Roth options; contributions deposited every payroll
- Paid Time Off \& Flexibility
- PTO accrued at 13% of hours worked ( 6 weeks / 30 days annually for full\-time employees)
- Up to 240 hours carryover
- Flexible option to adjust PTO and/or 401(k) contributions in exchange for salary adjustments
- Health \& Wellness
- Medical, dental, and vision insurance with significant company contribution
- CareFirst medical plans:
+ Platinum POS – $0 deductible
+ High Deductible POS – $1,600 / $3,200 deductible
- Company contributes 25% of deductible to HSA quarterly
- HSA and FSA options available
- Insurance (Company\-Paid)
- $50,000 Life Insurance
- $50,000 AD\&D
- Short\-Term Disability: 60% pay (up to $1,000/week)
- Long\-Term Disability: 60% pay (up to $6,000/month)
- Flexible Schedule
- Hourly pay with flexible/alternate schedules (customer approval required)
- Education \& Training
- Up to $5,000 annually for certifications, courses, conferences, books, and related expenses
- Additional Perks
- $100 annually toward company\-branded apparel
Why Join Us?
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This opportunity offers the chance to support complex, secure network environments where reliability, performance, and security are critical. You will work with experienced technical teams, provide hands\-on engineering support, and contribute directly to the design and operation of enterprise\-scale network infrastructure.
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 Staffed4U, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Staffed4U AI Hiring
Staffed4U has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Annapolis Junction, MD, 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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