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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
Senior Full Stack Engineer
Give the agents something real to reach for….
Peraton | Reston, VA
Peraton is seeking a Senior Full Stack Engineer to join our team in Reston, VA.
We're hiring a Full Stack Engineer SME to engineer the next generation of agentic AI platforms— the integrations, connectors, and orchestration plumbing that let our agents reach out to external APIs, enterprise systems, document repositories, and knowledge graphs and actually get something done with what comes back. This is hands-on product engineering on a platform that Peraton mission teams and federal customers use every day.
Why This Role Stands Out
- Wire agents to the real world. Every integration you ship expands what our agentic platform can do — new data sources, new tools, new autonomous workflows for analysts and operators.
- Work on the leading edge of technology. Python 3.12+, FastAPI, React, Postgres, Docker, and the latest AI models across OpenAI, Anthropic, Google, and more.
- Build next-generation agentic tools is the norm. Agentic flows, human-in-the-loop, evals — you'll be practicing this daily with our customers.
- Product-centric mindset. FedRAMP, FIPS, IronBank, OPA are in scope, and we ship like a product org.
What You'll Do
- Design and build integrations between our agents and external sources — public APIs, licensed feeds, partner systems, customer enterprise systems, document repositories, and structured databases.
- Package those integrations as agent-callable tools: clean interfaces, predictable I/O, sound error handling, and documentation the agents (and the humans configuring them) can actually rely on.
- Own the production concerns that make external data trustworthy — auth, pagination, rate limiting, retry/backoff, schema mapping, deduplication, caching.
- Build and evolve orchestration for multi-step agent workflows, including tool routing, parallel execution, and human-in-the-loop checkpoints.
- Contribute across the stack — FastAPI services, React UI for tool configuration and observability, Postgres schemas, containerized deploys.
- Instrument what you ship. Traces, metrics, evals, and regression tests for non-deterministic agent behavior.
- Partner with platform, security, and mission engineers to move capabilities from prototype to production inside federal compliance boundaries.
Qualifications
What You Bring
Must-Haves
- BS degree with 8 years professional experience, Associates degree with 10 years professional experience or MS degree with 6 years professional experience, or high school diploma/equivalent and ten years professional experience with meaningful exposure to AI/ML systems or LLM-based products
- Hands-on experience building with AI agents — multi-step reasoning, tool use, RAG pipelines, or autonomous task execution — and integrating LLMs into production or near-production applications
- Demonstrated experience designing and building integrations to external sources of information — public APIs, licensed data feeds, partner systems, customer enterprise systems, web content, document repositories, or structured databases
- Demonstrated experience packaging external capabilities as agent tools or skills with clean interfaces, predictable inputs and outputs, sound error handling, and documentation that AI agents and the humans configuring them can rely on
- Strong Python skills (3.12+); comfort with async/await patterns, type hints, and modern Python tooling
- Comfort working across the stack: FastAPI/Python backends, React frontends, Docker containerization, and PostgreSQL
- Experience building and consuming web APIs (REST, GraphQL, or comparable) and handling the integration concerns that make external data trustworthy in production — authentication, pagination, rate limiting, retry/backoff, schema mapping, deduplication, and caching
- Experience with workflow or task orchestration systems (Airflow, Prefect, Celery, or similar distributed execution frameworks) or comparable agent orchestration patterns
- Familiarity with agentic frameworks and an understanding of the underlying concepts (chains, tool calling, agent loops) that transfer across tools
- Experience working with LLM APIs (OpenAI, Anthropic, AWS Bedrock, or similar)
- Solid grounding in the security concerns specific to agentic and external integration work — secrets management, OAuth and API-key handling, rate-limit etiquette, defensive parsing, and prompt-injection awareness
- A product mindset: you think about the end user, not just the technical implementation
- Comfort operating with some ambiguity in a fast-moving environment
- US Citizenship is a requirement for this position
Nice-to-Haves
- Production experience with agent frameworks (LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, OpenAI Agents SDK, Anthropic tool-use, or comparable) and a sound point of view on when each is the right tool
- Experience designing and operating retrieval-augmented generation systems — chunking strategies, embedding models, vector stores, hybrid retrieval, reranking, and grounding/citation patterns
- Experience integrating with knowledge graphs, document stores, or curated content repositories as agent-accessible sources
- Experience evaluating agent and LLM systems — building eval harnesses, golden datasets, regression testing, and observability for non-deterministic systems
- Exposure to orchestration patterns: supervisor agents, parallel tool calls, human-in-the-loop flows, DAG-based pipeline execution
- Experience building plugin or extension systems: dynamic code loading, container isolation, API mixin patterns
- Familiarity with prompt engineering, evaluation frameworks, or agent observability
- Familiarity with federal compliance environments: FedRAMP, FIPS 140-2/3, IronBank container hardening, OPA policy enforcement, or Section 508 accessibility
- Experience with observability tooling: OpenTelemetry, Jaeger, Prometheus, Grafana, or similar distributed tracing/metrics stacks
- Experience with container orchestration (Docker SDK, Kubernetes) and distributed storage (S3, MinIO, JuiceFS)
- Prior work building internal tooling, enterprise automation products, or platforms for government customers
Why Peraton
Peraton is a next-generation national security company delivering mission capabilities across defense, intelligence, space, cyber, and federal civilian markets. The work you do here compounds across every Peraton mission that plugs into our agentic platform.
Ready to Build?
If you've been waiting for an agentic role where the integrations, the tools, and the guardrails all matter equally — apply. We want to see what you'd wire up first.
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. 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 $146K-$234K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $146K to $234K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Peraton AI Hiring
Peraton has 16 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, Research Scientist, Data Scientist. Positions span Albany, NY, US, Laurel, MD, US, College Park, MD, US. Compensation range: $128K - $304K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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