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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 Platform & Agentic Automation Engineer to design, develop, integrate, and maintain shared AI capabilities and intelligent workflow automation across enterprise platforms. This role focuses on integrating approved foundation models and managed AI services through reusable APIs, agentic workflows, and developer-facing tools rather than developing foundation models from scratch.
The ideal candidate combines strong software engineering and cloud expertise with experience integrating AI services into secure, automated workflows. The engineer will collaborate with platform, DevSecOps, identity, cybersecurity, and operations teams to deliver reliable, auditable, and secure AI-enabled capabilities.
Responsibilities
AI/ML Platform and Model-Service Integration
- Integrate approved foundation models and managed AI services, including AWS Bedrock or comparable platforms, into enterprise applications and workflows.
- Develop reusable APIs, service wrappers, and integration patterns that enable consistent access to model capabilities.
- Implement configurable model-selection and integration approaches that support model updates without requiring extensive changes to consuming applications.
- Support deployment and integration within cloud and restricted environments where approved AI services are available.
Agentic Workflow Engineering
- Design and implement agentic workflows to automate repeatable platform engineering, service management, and operational processes.
- Integrate AI agents with approved tools and APIs, including IT service management platforms, source control systems, infrastructure automation, knowledge repositories, and observability services.
- Establish appropriate workflow boundaries, approval requirements, tool permissions, auditability, and failure-handling mechanisms.
- Develop reusable automation components for account provisioning, access requests, environment deployment, incident enrichment, technical documentation retrieval, and other approved use cases.
Developer Assistance and Self-Service Automation
- Develop and maintain AI-powered developer assistants, chat interfaces, and low-code/no-code capabilities.
- Connect developer-facing tools to approved technical documentation, platform guidance, service catalogs, code examples, and engineering resources.
- Integrate AI assistance into development workflows while protecting credentials, sensitive information, and privileged platform functions.
- Collaborate with platform and developer-support teams to identify repetitive requests that can be addressed through self-service tools and guided automation.
Security and Enterprise Platform Integration
- Integrate AI services with identity and access management, role- and attribute-based access controls, service identities, secrets management, and audit mechanisms.
- Enforce established security boundaries, access policies, and tenant isolation requirements across AI services and agent workflows.
- Coordinate with cybersecurity teams to implement appropriate guardrails, data-handling controls, logging, and authorization requirements.
- Instrument AI services and automated workflows to support monitoring of availability, usage, failures, latency, and performance.
Software Engineering and Lifecycle Support
- Maintain AI platform components, workflow definitions, prompts, tool integrations, and configuration through source control and established engineering review processes.
- Develop automated tests and evaluation scenarios to validate critical workflows before deployment to operational environments.
- Troubleshoot model-service integrations, agent behavior, workflow failures, and developer-assistance capabilities.
- Evaluate approved model and service updates and coordinate controlled implementation with AI/ML operations, assurance, and platform engineering teams.
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 software engineering, platform engineering, cloud engineering, workflow automation, applied AI/ML engineering, or a related technical discipline.
- Hands-on experience integrating applications with foundation models or managed AI services through APIs.
- Strong software development or scripting skills in Python or a comparable programming language.
- Experience developing workflow automation that integrates with external tools, services, or APIs.
- Experience with AWS services and cloud-native application development patterns.
- Familiarity with Git-based development, CI/CD pipelines, containers, and modern software delivery practices.
- Understanding of authentication, authorization, secrets management, and secure service integration.
- Demonstrated troubleshooting skills across distributed applications and automated workflows.
- Ability to collaborate effectively with software, platform, cybersecurity, and operations teams.
Desired Qualifications
- Hands-on experience with AWS Bedrock or comparable managed foundation-model services.
- Familiarity with Bedrock Agents or other agent-orchestration frameworks.
- Experience implementing retrieval-augmented generation (RAG) using approved enterprise data sources.
- Experience integrating ServiceNow, GitLab, or comparable enterprise platforms through APIs.
- Familiarity with Terraform, CloudFormation, Ansible, or other infrastructure automation tools.
- Experience with OpenTelemetry or comparable application instrumentation and observability tools.
- Familiarity with Kubernetes and container orchestration.
- Experience working in classified, restricted, or otherwise highly regulated cloud environments.
- Experience evaluating and testing large language model (LLM) applications, prompts, and agentic workflows.
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 MLOps Engineer roles in our dataset (median: $172K across 22 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 11,520 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Peraton, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $205,000 based on 171 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($145K) sits 29% 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 MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
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).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
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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