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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 Full\-Stack Platform Engineer to help build and scale a platform that enables rapid development of secure, AI\-powered applications through natural language interfaces, agent builders, and mission\-level workflow orchestration.
This platform is intended to provide a modern, efficient alternative to traditional data intelligence and application delivery approaches by centralizing common capabilities such as security, access control, reusable platform services, and Agentic AI implementation patterns. The product will help development teams transition toward AI\-enabled, spec\-based engineering workflows, and accelerating delivery of solutions across a wide range of mission and business use cases.
In this position, you will help establish the platform foundation that enables teams to rapidly build and deploy secure Agentic AI solutions. Your work will reduce duplication, accelerate delivery, improve governance, and make advanced AI capabilities more accessible to developers and mission users alike. This role is best suited for a senior cloud/platform engineer who can build secure, scalable, cloud\-native platform capabilities and help establish the technical foundation for enterprise\-wide adoption of Agentic AI. Key responsibilities may include, but are not limited to:* Design, build, deploy, and maintain core platform services supporting an Agentic AI platform in cloud environments such as AWS, Azure, or similar
- Engineer scalable platform capabilities for agent orchestration, workflow execution, API integration, identity and access control, observability, and secure application enablement
- Build reusable infrastructure and shared services that reduce developer overhead and accelerate delivery of AI\-enabled applications
- Develop cloud\-native solutions using containerization, microservices, API\-first design, infrastructure\-as\-code (IaC), and automated deployment pipelines
- Implement secure platform controls including authentication, authorization, secrets management, auditing, and policy enforcement
- Partner with software engineers, architects, product teams, and security stakeholders to define platform requirements, technical standards, and implementation priorities
- Improve platform reliability and operational maturity through monitoring, logging, tracing, alerting, and performance optimization
- Support integration of AI/LLM capabilities into a cohesive platform architecture
- Troubleshoot complex platform, cloud, and deployment issues in development and production
- Document platform architecture, engineering standards, and operational best practices
- Contribute as a senior technical team member by helping shape platform direction and mentoring other engineers as needed
- Design and implement RESTful API endpoints using FastAPI, maintain API contracts with frontend consumers, and implement OpenAI\-compatible API endpoints with token streaming
- Develop and extend the plugin lifecycle system: packaging, validation, deployment, activation, and lifecycle hook execution across worker, service, and library plugin types
- Manage database operations using SQLModel/SQLAlchemy with PostgreSQL, implement schema migrations, and maintain configuration management
- Write unit and integration tests, enforce code quality), and contribute to CI/CD pipelines
- Collaborate with frontend engineers to deliver cohesive user experiences, and where possible, contribute to frontend development of platform UI supporting components and frameworks
\#px2026##### QUALIFICATIONS
Required Qualifications
- Minimum of BS with 12\+ years of experience, MS with 10\+ years of experience or PhD with 7\+ YoE in Computer Science, Computer Engineering, Software Engineering, Information Systems, or related technical field
- 7\-10 years of relevant experience in platform engineering, cloud engineering, DevOps, infrastructure engineering, backend systems, or related technical roles
- Hands\-on experience with major cloud platforms such as AWS, Azure, or Google Cloud
- Strong experience building and operating cloud\-native platforms or distributed systems in production environments
- Experience with Docker, Kubernetes, and modern container\-based deployment models
- Experience with infrastructure\-as\-code (IaC) using tools such as Terraform
- Strong understanding of CI/CD, automated testing, deployment pipelines, and release engineering best practices
- Experience implementing enterprise\-grade platform security capabilities such as RBAC, IAM, secrets management, encryption, and policy controls
- Strong proficiency in Python (5\+ years, with async/await experience); additional proficiency in Java, JavaScript/TypeScript or other languages preferred for full\-stack contributions
- Production experience with FastAPI or similar async web frameworks, and hands\-on experience designing and integrating RESTful APIs in secure, scalable environments
- Experience with the monitoring, logging, metrics, and operational support for production systems
- Ability to work across technical teams and translate requirements into well engineered solutions
- Strong written and verbal communication skills
- Experience with PostgreSQL and ORM tools (SQLModel, SQLAlchemy, or equivalent)
- Experience with pytest or similar testing frameworks and writing maintainable test suites
- Understanding of distributed systems concepts (eventual consistency, idempotency, retry patterns)
- US Citizenship is required for this position
Desired Qualifications* Experience supporting AI/ML or LLM\-enabled platforms
- Familiarity with Agentic AI, including agent orchestration, tool use, workflow chaining, and human\-in\-the\-loop execution models
- Experience building multi\-tenant developer platforms or shared services used by multiple domains or modalities
- Experience with platform patterns that support self\-service application development or workflow\-based solution delivery
- Familiarity with modern AI development tooling (LangChain, LangGraph, or similar agent frameworks), model integration patterns, or orchestration frameworks such as Airflow or Prefect
- Experience with event\-driven architecture, messaging systems, and asynchronous processing
- Experience optimizing platform scalability, reliability, and cloud cost
- Experience supporting commercial product delivery, or enterprise platform adoption across multiple business areas
- Experience defining platform standards, reusable engineering patterns, and technical roadmaps
- Experience with OPA (Open Policy Agent) or policy\-based access control and authorization
- Experience with OpenTelemetry and observability tooling (Jaeger, Prometheus, Grafana)
- Experience with Kafka, RabbitMQ, ZeroMQ, gRPC, or similar messaging and RPC systems
- Frontend development experience with modern JavaScript frameworks (React, Vue, or similar) — strongly preferred for full\-stack contributions
- Experience with product line, plugin, or extension architectures involving dynamic loading, sandboxed execution, and lifecycle management
##### 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. Senior-level AI roles across all categories have a median of $227,400. 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.
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