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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 is looking for a Director, Digital Transformation, AI, and Data Architect to join the Corporate Growth, Enterprise Capabilities and Solutions team as one of the technical leads behind our most competitive pursuits. You will provide thought leadership and design differentiated service offerings, architectures, and technical strategies for digital modernization, artificial intelligence, and data\-driven mission outcomes. These provide the people and process elements to the Peraton Labs technology developments. You will work directly alongside capture teams, growth leaders, sector CTOs, and technical SMEs from early strategic opportunity shaping through proposal submission to build solutions that win contracts and frameworks that make winning repeatable. If you are energized by high\-stakes technical challenges and want to see your work translate directly into contract wins, this role was built for you.
What you’ll do:
- Lead the Peraton Enterprise Capabilities and Solutions Portfolio for Digital Modernization, Artificial Intelligence, and Data from strategy through execution, ensuring every offering is aligned to business priorities, technically sound, and built to compete across all Peraton customer’s market spaces.
- Work directly with capture teams, sector growth leaders, and SMEs to shape Digital Transformation and AI/Data solutions from early opportunity identification through proposal submission.
- Develop technical discriminators, solution narratives, and competitive positioning materials that support capture and proposal efforts across the enterprise, with deep credibility in digital modernization, AI/ML architecture, and data platform design.
- Design and develop scalable, reusable digital transformation and AI/Data service offerings, architectures, and technical frameworks that give Peraton's sector teams a competitive edge on federal pursuits.
- Continuously evolve the portfolio by analyzing capability gaps, technology readiness, and market trends across federal digital modernization and AI adoption to identify portfolio enhancements and competitive differentiation opportunities that strengthen Peraton's position in core markets.
- Provide thought leadership and author white papers and other solution artifacts to support customer engagement efforts highlighting newly developed frameworks and Peraton Labs applicable technologies.
- Partner with sector growth teams, business stakeholders, and engineering leadership to translate market opportunities into viable, differentiated service offerings.
- Drive the design and architecture of services across the portfolio. Ensure each service is fully defined with supporting sales assets, cost models, and solution guides that equip sector teams to sell and deliver.
- Present technical strategies, AI/Data architecture roadmaps, and digital modernization solution recommendations to customers, capture teams, and executive leadership with the clarity and confidence to drive decisions.
- Engage with key customers, industry partners, and technology vendors to stay ahead of evolving federal AI policy, emerging data platform capabilities, and competitive dynamics in Peraton's target markets.
##### Qualifications
Required Qualifications:
- 16\+ years of industry experience
- Deep expertise in AI/ML platform architecture including model development, training, and deployment pipelines, with demonstrated experience integrating AI and generative AI capabilities into enterprise or mission applications
- Proven experience designing and implementing modern data platform architectures including data lakes, data mesh, and data fabric, with strong command of data governance, data quality, and data management frameworks in a federal environment
- Demonstrated experience with MLOps practices and AI lifecycle management, from development and testing through production deployment and continuous monitoring
- Demonstrated experience with Data Management Body of Knowledge (DMBoK), Data Management Capability Assessment Models (DCAM), Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles, and/or Control Objectives for Information and Related Technologies (COBIT)
- Working knowledge of legacy system modernization strategies, application migration approaches, and API\-first and microservices\-based architecture patterns that accelerate digital transformation
- Demonstrated experience applying human\-centered design principles and iterative user research methods to federal digital transformation programs, with measurable outcomes in user adoption and mission effectiveness
- Direct experience supporting federal government customers in a contractor environment, with a working understanding of mission requirements, acquisition processes, and the competitive federal market
- Proven experience contributing to competitive federal pursuits, including technical solution development, architecture design, or competitive positioning in support of capture or proposal efforts
- Demonstrated ability to translate complex AI, data, and digital transformation concepts into compelling solution narratives for capture teams, executive leadership, and government evaluators
- A demonstrated bias for action and a track record of delivering technical work that contributed directly to competitive wins.
- US citizenship and ability to obtain a Top Secret security clearance
Desired Qualifications:
- Active TS/SCI with Polygraph eligibility
- Direct experience in capture management, proposal development, or technical volume leadership on large\-scale, competitive federal digital transformation or AI/Data bids
- Familiarity with Responsible AI and algorithmic accountability frameworks aligned to OMB AI policy and Executive Order 14110, including experience applying AI ethics and bias mitigation practices in federal program contexts
- Experience with federated learning and privacy\-preserving AI techniques for sensitive or classified data environments where centralized model training is not feasible
- Working knowledge of AI\-ready data infrastructure including vector database architecture and retrieval\-augmented generation (RAG) frameworks supporting large language model deployment in federal environments
- Experience with synthetic data generation techniques for AI model training in data\-scarce or operationally sensitive federal environments
- Familiarity with edge AI and on\-device inference architectures supporting disconnected, intermittent, or contested operational environments
- Experience with digital twin modeling applied to mission systems, infrastructure, or operational environments
- Familiarity with multimodal AI architectures integrating text, imagery, signals, and sensor data for mission applications
- Experience presenting AI/Data strategies and digital transformation roadmaps directly to senior government customers or evaluators
- Prior experience developing competitive positioning materials, technical discriminators, or solution playbooks that supported pursuit efforts in the federal AI or digital transformation market
- Relevant certifications such as AWS Machine Learning Specialty, Google Professional Machine Learning Engineer, Azure AI Engineer, or relevant data architecture and governance certifications (e.g, CDMP)
##### Details
Target Salary Range: $190,000 \- $304,000\. 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 $190K-$304K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($247K) sits 15% above the category median. Disclosed range: $190K to $304K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Peraton AI Hiring
Peraton has 10 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, AI Software Engineer. Positions span Red Bank, NJ, US, Herndon, VA, US, Basking Ridge, NJ, US. Compensation range: $166K - $304K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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.
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