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.
##### Program Overview
Leading provider of Information Operations (IO) and Irregular Warfare (IW) capabilities to the U.S. government. Integrates Information Related Capabilities (IRC) across multiple domains, environments, and geographic boundaries, supporting both domestic and overseas operation via Information Operations planners, intelligence analysts, cultural advisors, and linguists
##### About The Role
Peraton is hiring: Generative AI Specialist / AI \& ML Developer
Location: Remote
About the Role
- Peraton is seeking an experienced Generative AI Specialist to support high\-impact Internal Research and Development (IRAD) initiatives focused on AI/ML\-driven platforms for Operations in the Information Environment (OIE).
- This role is central to delivering advanced generative AI capabilities supporting COCOM information operations across CENTCOM, NORTHCOM, INDOPACOM, AFRICOM, and EUCOM.
- You will drive innovation in LLMs, agentic AI workflows, and generative AI application development—directly influencing Peraton’s competitive position in the defense AI landscape.
Responsibilities:
You will:
- Design and optimize generative AI solutions using GPT 4, Claude, Gemini, and Azure OpenAI, including prompt engineering, fine\-tuning, and retrieval augmented generation (RAG).
- Implement agentic LLM architectures with structured output (JSON/GeoJSON), chain\-of\-thought/chain\-of\-debate methods, and multi\-agent orchestration.
- Build and maintain prompt libraries, evaluation frameworks, and QA pipelines for defense\-grade AI content.
- Integrate generative AI into existing platforms via well\-documented APIs, ensuring interoperability with DoD systems including Maven, C2IE, and IRIS.
- Research emerging generative AI techniques including multimodal models, synthetic data generation, and AI\-assisted analysis for information operations.
- Translate COCOM operational requirements into deployable AI solutions; support TTX events and platform demonstrations.
- Implement responsible AI practices including bias detection, validation, hallucination mitigation, and HITL workflows.
- Support MVP\-aligned milestone delivery, contributing to TRL progression and ROI measurement.
Document novel prompting techniques, model configurations, and workflows that may constitute IP or trade secrets.
*
Key Technologies \& Platforms:
GPT 5, Claude, Gemini, Azure OpenAI Service, LangChain, LlamaIndex, Hugging Face Transformers, Python, Pinecone, ChromaDB, Weaviate, pgvector, AWS, Azure, Docker, Kubernetes, REST APIs, JSON/GeoJSON, Plotly, D3\.js, IRIS, Maven, C2IE, OMEGA, FedRAMP IL2–4\.
##### Qualifications
Minimum Qualifications:
- Bachelor’s degree in a related technical field \+ 5 years relevant experience.
+ An additional 4 years will be considered in lieu of the bachelors degree requirement.
- 3\+ years in software development or data science, including 2\+ years focused on generative AI, LLMs, or NLP.
- Hands\-on experience with foundation models (GPT 4, Claude, Gemini, Llama, Mistral) including prompt engineering, few\-shot learning, fine\-tuning, and API integration.
- Strong Python skills; experience with LangChain, LlamaIndex, Hugging Face, OpenAI/Anthropic APIs.
- Experience building RAG systems using vector databases (Pinecone, Weaviate, ChromaDB, pgvector).
- Cloud experience (AWS, Azure) and deploying AI/ML models in production environments.
- Understanding of AI safety, hallucination mitigation, bias reduction, and prompt\-injection protection.
- Experience with Git, CI/CD, and DevSecOps practices.
- U.S. citizenship required.
- Ability to obtain Secret and final TS/SCI security clearances.
- Current, valid U.S. passport for potential OCONUS travel.
Preferred Qualifications:
- Master’s degree.
- Experience with agentic AI architectures, multi\-agent workflows, and LLM tool\-use.
- Hands\-on experience with fine\-tuning, RLHF, DPO, LoRA/QLoRA.
- Familiarity with multimodal models and defense/intelligence applications.
- Experience with IRIS, OMEGA, or similar operational platforms.
- Background supporting IO, PSYOP, influence analysis, or COCOM operations.
- Knowledge of MLOps, A/B testing, and LLM evaluation frameworks (RAGAS, DeepEval).
- Experience with synthetic data generation, simulation, or Monte Carlo methods.
- Understanding of geospatial data formats (GeoJSON, KML) and visualization tools (Plotly, D3\.js).
- Strong communication skills for technical\-to\-nontechnical translation and customer demos.
- Relevant cloud/AI certifications (AWS, Azure, Google, DeepLearning.AI).
Key Technologies \& Platforms:
GPT 5, Claude, Gemini, Azure OpenAI Service, LangChain, LlamaIndex, Hugging Face Transformers, Python, Pinecone, ChromaDB, Weaviate, pgvector, AWS, Azure, Docker, Kubernetes, REST APIs, JSON/GeoJSON, Plotly, D3\.js, IRIS, Maven, C2IE, OMEGA, FedRAMP IL2–4\.
##### Details
Target Salary Range: $135,000 \- $216,000\. 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 $135K-$216K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2064 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 3,963 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $180,000 based on 12,398 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400. Disclosed range: $135K to $216K.
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 ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Peraton AI Hiring
Peraton has 26 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, Data Scientist. Positions span Macdill AFB, FL, US, Herndon, VA, US, McLean, VA, US. Compensation range: $128K - $234K.
Location Context
Across all AI roles, 15% (593 positions) offer remote work, while 3,349 require on-site attendance. Top AI hiring metros: New York (2,585 roles, $210,300 median); San Francisco (2,103 roles, $253,000 median); Los Angeles (1,764 roles, $190,500 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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 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 ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $290,000 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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.