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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
Peraton Labs is seeking a Senior DevSecOps / Platform Engineer to own the Agentic AI platform end\-to\-end across delivery, infrastructure, runtime operations, and platform health. This role will be responsible for building and operating the technical backbone that enables secure, reliable, and scalable platform delivery, from CI/CD pipelines and automated testing through EKS cluster operations, AWS infrastructure, monitoring, alerting, and synthetic health checking. The ideal candidate combines deep technical execution with strong operational judgment and a builder mindset, with the ability to design systems that are not only functional, but supportable, observable, secure, and audit\-ready.
This is an opportunity to own the platform layer of a highly visible and ambitious Agentic AI environment. You will work on meaningful problems that require strong technical judgment, operational rigor, and modern engineering practices. For the right candidate, this role offers the chance to build real systems, improve platform resilience, and directly influence how advanced AI\-enabled capabilities are delivered in secure, compliant, and production\-ready ways. Key responsibilities may include, but are not limited to:* Own and operate the Agentic AI platform end\-to\-end across CI/CD, cloud infrastructure, Kubernetes operations, observability, and runtime reliability
- Design, build, and maintain CI/CD pipelines across GitHub Actions and/or GitLab CI, enabling secure, repeatable, and efficient delivery workflows
- Implement and improve automated testing and quality gates within the software delivery lifecycle, including build validation, integration checks, security testing, and deployment controls
- Lead operational ownership of Amazon EKS / Kubernetes environments, including cluster lifecycle management, upgrades, troubleshooting, RBAC, Helm\-based deployments, pod identity, and GitOps\-aligned workflows
- Build and maintain AWS infrastructure supporting platform and application needs across services such as VPC, IAM, S3, RDS, CloudTrail, and KMS
- Own infrastructure provisioning and lifecycle management through OpenTofu / Terraform and related infrastructure\-as\-code practices
- Design and operate a mature monitoring, logging, and alerting stack using CloudWatch, Prometheus/Grafana, or equivalent tooling
- Develop actionable alerts, service health indicators, and SLO\-aligned operational thresholds that support fast triage and resilient service delivery
- Build and maintain synthetic and transactional monitoring that exercises real authentication flows, user journeys, and critical service transactions
- Implement and maintain DAST and runtime security testing in delivery pipelines using tools such as OWASP ZAP, Burp, Nuclei, or equivalent
- Support security\-focused CI/CD practices including secrets management, least privilege, software supply chain integrity, and audit evidence generation
- Drive modern engineering delivery patterns such as trunk\-based development, merge\-request\-driven change, and automated release quality controls
\#px2026##### QUALIFICATIONS
Required Qualifications
- Minimum of BS with 8\+ years of experience, MS with 6\+ YoE, or PhD with 3\+ YoE in platform engineering, DevOps, SRE, or closely related infrastructure engineering roles
- Deep CI/CD experience with GitHub Actions and/or GitLab CI
- Strong hands\-on background operating Amazon EKS / Kubernetes environments
- Senior\-level AWS experience across core services including VPC, IAM, S3, RDS, CloudTrail, and KMS
- Strong experience building and operating monitoring, logging, and alerting solutions at scale using CloudWatch, Prometheus/Grafana, or equivalent
- Hands\-on infrastructure\-as\-code experience using OpenTofu / Terraform
- Experience supporting agile delivery models, including trunk\-based development, MR\-driven change, and automated quality gates
- Strong security and compliance engineering foundation, including secrets management, least privilege, supply chain integrity, and audit evidence support
- Experience implementing automated testing in CI/CD pipelines
- Experience running DAST and security testing against live services in pipeline workflows, including triage and gating of findings alongside SAST/SCA results
- Experience building synthetic and transactional monitoring, including scripted health checks that validate real auth flows and critical transactions
- Strong troubleshooting, systems thinking, and operational ownership mindset
- US Citizenship is a requirement for this position
Preferred Qualifications* Experience implementing BigBang / Platform One
- Familiarity with Iron Bank, Sigstore/Cosign, and hardened image pipeline practices
- Prior experience supporting systems pursuing or maintain an ATO
- Experience with contract testing such as PACT
- Experience with chaos engineering concepts or tooling
- Experience with progressive delivery approaches such as canary or blue/green deployments with automated rollback based on health\-check failure
- Experience working in regulated or mission environments aligned to NIST 800\-171, CMMC, FedRAMP, or DoD security expectations
- Background helping mature platform reliability and security in cloud\-native environments supporting sensitive workloads
##### DETAILS
Target Salary Range: $112,000 \- $179,000\. 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 $112K-$179K range is below 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($145K) sits 19% below the category median. Disclosed range: $112K to $179K.
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
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