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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
We are seeking an AI/ML Operations & Assurance Engineer to monitor, evaluate, and maintain production AI-enabled applications, foundation-model integrations, and agentic workflows. This role focuses on operational reliability, workflow performance, evaluation and regression testing, configuration control, usage and cost monitoring, and the safe introduction of AI-related changes into production environments.
The ideal candidate combines cloud and application operations experience with an understanding of large language model (LLM) applications, agent behavior, and AI-specific operational risks. The engineer will collaborate with AI/ML developers, site reliability engineers, cybersecurity teams, and platform operations personnel to maintain reliable, auditable, and secure AI-enabled services.
Responsibilities
Production Monitoring and Operations
- Monitor availability, latency, error rates, model and token usage, service costs, tool-call failures, and workflow completion metrics for production AI services.
- Integrate AI service health and agent-workflow telemetry into observability dashboards, monitoring systems, and alerting processes.
- Investigate failed, stalled, repetitive, or abnormal AI workflows and coordinate remediation with software, platform, and AI engineering teams.
- Maintain operational runbooks, escalation procedures, troubleshooting guidance, and recovery processes for AI-enabled applications.
- Distinguish infrastructure and application failures from issues associated with model behavior, prompts, retrieval sources, or agent orchestration.
AI Evaluation and Operational Assurance
- Develop and maintain repeatable evaluation scenarios for critical AI workflows, developer assistants, and model-service updates.
- Assess task completion, output quality, error patterns, inappropriate tool use, and other indicators of operational suitability.
- Coordinate regression testing before changes to models, prompts, tools, workflow logic, or retrieval sources are deployed to production.
- Analyze user feedback and operational results to identify where workflows require refinement, additional controls, or restrictions on automated execution.
- Document evaluation results, limitations, and recommendations to support evidence-based deployment decisions.
Change and Configuration Management
- Maintain version control and change records for prompts, agent instructions, tool definitions, model selections, evaluation datasets, and workflow configurations.
- Coordinate model and service updates with AI engineering, cybersecurity, platform operations, and architecture stakeholders.
- Support rollback to validated configurations when changes negatively affect reliability, performance, or workflow quality.
- Ensure AI-related changes follow established review, testing, approval, and deployment procedures.
- Monitor configuration drift and verify that production services remain aligned with approved configurations.
Security, Audit, and Compliance Support
- Coordinate with cybersecurity and identity teams to verify access controls, tool permissions, logging, and auditability for production AI services.
- Support investigations involving anomalous AI behavior, unauthorized actions, or potential data-handling concerns.
- Collect and maintain operational evidence supporting security assessments, continuous monitoring, and service reviews.
- Verify that production workflows use approved models, endpoints, tools, and data sources in accordance with established policies.
- Support the implementation and validation of operational safeguards for AI-enabled functions.
Operational Improvement and Reporting
- Identify repetitive operational tasks and support activities suitable for controlled AI-assisted automation.
- Measure the impact of deployed workflows on processing time, support workload, service quality, and operational efficiency.
- Collaborate with service delivery, Tier 2 support, SRE, and platform engineering teams to improve workflows based on operational demand.
- Report trends in service health, model usage, operating costs, evaluation results, and unresolved issues.
- Recommend improvements to monitoring, testing, configuration control, and operational procedures.
Location: This position is fully onsite in Chantilly, VA.
Qualifications
Required Qualifications
- Active TS/SCI clearance with CI Polygraph.
- BS/BA and 5 years of experience, an additional 4 years of experience may be considered in lieu of a degree.
- Approximately 5–8 years of experience in platform operations, site reliability engineering, cloud engineering, software operations, applied AI/ML operations, or production application support.
- Experience operating and troubleshooting production software applications or cloud services.
- Experience with monitoring, logging, metrics, incident response, and configuration or change management.
- Experience evaluating software behavior through repeatable test cases, regression testing, quality measures, or operational performance analysis.
- Working knowledge of foundation-model or LLM-based applications, including prompts, model endpoints, tool calls, and retrieval-augmented applications.
- Experience with AWS or comparable cloud platforms.
- Strong analytical, troubleshooting, documentation, and communication skills.
Desired Qualifications
- Experience with AWS Bedrock or comparable managed foundation-model services.
- Familiarity with LLM or agent evaluation frameworks, automated evaluation harnesses, or AI application testing.
- Experience with OpenTelemetry, Amazon CloudWatch, OpenSearch, Prometheus, Grafana, or comparable observability technologies.
- Familiarity with ServiceNow, IT service management, and incident-management processes.
- Experience with GitLab, CI/CD pipelines, and controlled configuration promotion.
- Familiarity with Kubernetes and containerized application environments.
- Experience monitoring cloud utilization, model consumption, and service costs.
- Knowledge of risk management frameworks, continuous monitoring, or security evidence collection.
- Experience supporting classified, restricted, or highly regulated environments.
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. Use of artificial intelligence (AI) tools of any kind during Peraton interviews is strictly prohibited unless the candidate has obtained prior written authorization. All interview responses must be the candidate’s own.
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: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($145K) sits 31% below the category median. Disclosed range: $112K to $179K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Peraton AI Hiring
Peraton has 17 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, MLOps Engineer, Data Scientist. Positions span Laurel, MD, Chantilly, VA, US, Washington, DC, US. Compensation range: $166K - $282K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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