Interested in this AI/ML Engineer role at Peraton?
Apply Now →Skills & Technologies
About This Role
##### ABOUT PERATON
Peraton is a next\-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world’s leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees solve the most daunting challenges that our customers face. Visit peraton.com to learn how we’re keeping people around the world safe and secure.
##### ABOUT THE ROLE
Are you passionate about building cutting\-edge cloud solutions that support national security? Do you thrive in fast\-paced, mission\-driven environments? Peraton is seeking a talented and skilled Cloud Administrator to join our Cyber Intelligence team in Laurel, MD. As part of a collaborative, high\-impact integrated product development team, you’ll work on next\-generation cyber and AI/ML capabilities hosted on large\-scale compute clusters and AWS Cloud infrastructure. This is your chance to help shape innovative services that make a real difference. Interested in Artificial Intelligence and Machine Learning? If so, this is the position for you.
The Cloud Administrator provides support for implementation and troubleshooting and maintenance on large Hadoop/Cloud clusters. Competencies and Skills:* Experience with Large Language Models (LLM), Agentic AI, and Retrieval\-Augmented Generation (RAG)
- Expertise in AWS Cloud Architecture design and development
- Agile development experience with source code management practices and tools
- Well\-grounded in Linux fundamentals and knowledge in at least one scripting language (e.g. Python, Ruby, Perl, etc.)
- Knowledge of security and compliance best practices
The Level Cloud Administrator shall possess the following capabilities:* Shall have at least three (3\) years of experience managing and monitoring large distributed systems (\>1000 nodes)
- Shall have experience diagnosing and troubleshooting large scale cloud computing systems including familiarity with distributed systems for storage and retrieval of data e.g. Apache's Hadoop, Accumulo, CASSANDRA distributed databases, or SCALITY or SWIFT object stores.
- Shall have demonstrated ability to work within a pre\-defined mission focused team structure, follow SOP's, communicate effectively, accept constructive feedback and receive technical guidance and advice from senior level technical resources.
- Shall have demonstrated a willingness to learn new technologies and leverage senior level resources to expand current technical foundation using team structure.
- Demonstrated ability to work independently on complex tasks, show a willingness to educate and train more junior technical resources.
- Shall have five (5\) years of experience writing software scripts using scripting languages including Bash, Perl, or Python.
- Shall have seven (7\) years of experience demonstrating a fundamental understanding and working knowledge of core components of the Linux operating system including the management of user and group accounts in LDAP configuration of DHCP, DNS, and TFTP.
- Shall have demonstrated experience with configuration management tools including Puppet and SALT
- Expert understanding of the end\-to\-end Linux PXE/Network provisioning process to include familiarity with Anaconda Kickstart configurations, RAID controller utilities, TFTP images, and disk detect scripts.
- Experience accessing and troubleshooting systems via remote utilities to perform hardware diagnosis and repair including VNC, serial over LAN interfaces, and IPMI, BIOS\-level configuration.
- Understanding of overall corporate architecture as well as familiarity with openSSL and Java keystore manipulation.
- Expert in troubleshooting commodity hardware platforms including previous experiences with SGI/HP hardware including SGI's J series.
MPOJobs
\#AJCM \#PeratonRoyalMove
\#MDFSP Keywords: Accumulo, CASSANDRA, SCALITY, SWIFT. Scripting \- Bash, Perl, Python. LDAP configuration of DHCP, DNS, and TFTP. Puppet and SALT tools. Artificial Intelligence (AI), Large Language Model (LLM), Machine Learning (ML).##### QUALIFICATIONS
Basic Qualifications:The User Experience Designer shall possess the following capabilities:* Operate in an agile development environment requiring the delivery of User Experience feedback.
- Conduct User Experience research and optimization.
- Create final visual designs, storyboards, interaction flows, and interface guidelines.
- Take a user\-centered approach in crafting design solutions that meet both user needs and product strategy requirements.
- Collaborate with Product Owners to rapidly prototype and iterate on multiple design solutions.
- Review final implementation with front\-end developers to ensure design standards are followed.
- Analyze and provide solutions for project life cycle design including interviewing customers, clearly articulating customer requirements to design team, creating visual designs/prototypes, presenting information to customers, and helping solve and clarify questions and problems.
- Participate in several diverse engineering teams to conduct User Experience research and optimization.
- Collaborate with stakeholders within various customer bases to provide recommendations to other Designers.
Position Requirements (Experience and Education):* An Active TS/SCI clearance with polygraph is required
- Seven (7\) years of experience is required.
- A Bachelor's Degree in Engineering, Systems Engineering, Computer Science, Mathematics is required from an accredited college or university.
- Hadoop/Cloud System Administrator Certification or comparable Cloud System/Service Certification is required to have one of the following Cloud Developer Certifications:
+ AWS DevOps Engineer Professional
+ CDP Administrator \- Private Cloud Base
+ Certified Kubernetes Administrator (CKA)
Additional Desired Qualifications:
- Advanced knowledge of SSH tunneling and protocols, including the implementation of dynamic SOCKS proxies as well as other ssh\-based utilities, including rysn, pdsh, pdcp, and WinSCP.
- Basic understanding of low\-level network concepts including VLANs, port channel bonding and layer2/layer 3 switch interactions.
- Familiarity with software load balancers for large scale webservice implementations including HAProxy and NGINX
- Experience with Kubernetes orchestration services and Docker images.
- Experience with log aggregation and search tools including Elasticsearch, Logstash, filebeats, Grafana, and rsyslog.
##### DETAILS
Target Salary Range: $146,000 \- $234,000\. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.
Benefits Statement: Peraton offers eligible employees a variety of benefits including medical, dental, vision, life, health savings account, short/long term disability, EAP, parental leave, 401(k), paid time off (PTO) for vacation, and company paid holidays. A full listing of available benefits can be viewed at https://www.careers.peraton.com/benefits.
Application Statements: The application period for the job is estimated to be 30 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates. By applying to this job, you are expressing interest in the role and the Company. During the review of your application, you may be required to participate in an on\-camera interview, as well as participate in a process to verify your identity.
EEO: Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.
Salary Context
This $146K-$234K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Peraton, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($190K) sits 6% above the category median. Disclosed range: $146K to $234K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Peraton AI Hiring
Peraton has 16 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span Macdill AFB, FL, US, Laurel, MD, US, Basking Ridge, NJ, US. Compensation range: $128K - $234K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.