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About This Role
*Amentum is a global leader in advanced engineering and innovative technology solutions, trusted by the United States and its allies to address their most significant and complex challenges in science, security and sustainability. Our people apply undaunted curiosity, relentless ambition and boundless imagination to challenge convention and drive progress. Our commitments are underpinned by the belief that safety, collaboration and well\-being are integral to success. Headquartered in Chantilly, Virginia, we have approximately 50,000 employees in more than 70 countries across all 7 continents.*
The AI Solutions Architect — Programs \& Delivery is the senior operational architect for Amentum's AI Center of Excellence. This role owns the technical execution backbone of the COE: JANUS (the COE's AI intake and portfolio system of record), pilot and lighthouse delivery pipeline, and the interface between the COE hub and Amentum's operating\-group AI spokes.
The role bridges business demand, technical architecture, and governance to ensure AI initiatives are documented, planned, coordinated, and delivered against per\-lighthouse execution criteria — not just inventoried. This position Hybrid to our Reston, VA Office and US Citizenship is required.
Key Outcomes
- Operate JANUS as the COE's portfolio system of record — drive waves of pilot and lighthouse records to completeness, coordinate gold\-record certification, and prepare board\-ready evidence for Amentum’s AI Advisory Council.
- Drive technical delivery coordination across waves of lighthouse initiatives against signed charters and pilot execution criteria.
- Interface the COE hub with operating\-group spokes so reusable architecture patterns flow both directions enabling growth.
- Coordinate with AI Governance, Cyber, CIO/EA, Legal, Export Control, Finance, and Procurement liaisons to keep boundary approvals on schedule for lighthouse production paths.
- Track risks, dependencies, and decision\-package status across the AI portfolio; support COE leadership and the AI Advisory Council with timely, evidence\-backed status.
Core Responsibilities
- Own day\-to\-day operation of JANUS for COE intake, triage, prioritization, and lifecycle tracking of AI use cases.
- Translate business problems into technical AI solution architectures, identifying reusable patterns and platform components across AI Pilots.
- Support the AI Solution Architect — Generative AI (peer role) on integration, deployment, and operational hand\-off of GenAI capabilities into mission and enterprise workflows.
- Coordinate with spoke leads on lighthouse charter execution, surfacing blockers to the Chief AI Architect and the AI Advisory Council.
- Maintain the COE decision log and Council package readiness on a monthly cadence.
- Partner with vendor Forward Deployed Engineers (FDEs) during platform buildout, ensuring reusable patterns are documented and transitioned to internal owners with FDE exit criteria met.
- Maintain alignment between AI work and Amentum's two\-cloud posture (Azure Commercial for COE, Azure Government for customer\-mandated programs).
Knowledge, Skills \& Abilities:
- Strong program\-architecture judgment — ability to sequence work, identify dependencies, and call risks early.
- Ability to operate across executive (CTO, Council), technical (engineering, IT, data), and business\-unit (spoke) stakeholders.
- Excellent written and verbal communication; comfort preparing board\-level decision packages.
- Detail\-oriented in a fast\-moving environment; strong organizational and follow\-through skills.
Minimum Requirements
- Typically 5\+ years of progressive experience in AI / data program architecture, AI portfolio management, or enterprise solution delivery with a Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or related field. Master's preferred.
- Experience operating an enterprise intake / portfolio system for AI, data, or technology initiatives.
- Working knowledge of AI/ML solution architecture, including data pipelines, model integration patterns, and platform components.
- Hands\-on familiarity with cloud platforms (Azure preferred; AWS or GCP also acceptable).
- Experience working with structured and semi\-structured data, databases, and integration patterns across enterprise systems.
- US Citizenship is required.
Preferred Qualifications
- Experience in a federal services, government, or mission\-driven environment.
- Exposure to AI governance frameworks (model risk, responsible AI, NIST AI RMF, EU AI Act categorization).
- Background coordinating multi\-vendor AI engagements (RFI/RFP, Forward Deployed Engineering, hyperscaler partnerships).
- Experience with Azure AI services, Microsoft Copilot ecosystem, and/or AWS Bedrock.
Work Environment: Hybrid; collaborative technical/business environment spanning CTO Office and operating\-group spokes.
Work Schedule: Standard business hours with flexibility; occasional travel to spoke sites.
Compensation Details:
$160k \- $180k
The compensation range or hourly rate listed for this position is provided as a good\-faith estimate of what the company intends to offer for this role at the time this posting was issued. Actual compensation may vary based on factors such as job responsibilities, education, experience, skills, internal equity, market data, applicable collective bargaining agreements, and relevant laws.
Benefits Overview:
Our health and welfare benefits are designed to support you and your priorities. Offerings include:
- Health, dental, and vision insurance
- Paid time off and holidays
- Retirement benefits (including 401(k) matching)
- Educational reimbursement
- Parental leave
- Employee stock purchase plan
- Tax\-saving options
- Disability and life insurance
- Pet insurance
*Note: Benefits may vary based on employment type, location, and applicable agreements. Positions governed by a Collective Bargaining Agreement (CBA), the McNamara\-O'Hara Service Contract Act (SCA), or other employment contracts may include different provisions/benefits.*
Original Posting:
07/02/2026 \- Until Filled
Amentum anticipates this job requisition will remain open for at least three days, with a closing date no earlier than three days after the original posting. This timeline may change based on business needs.
Amentum is proud to be an Equal Opportunity Employer. Our hiring practices provide equal opportunity for employment without regard to race, sex, sexual orientation, pregnancy (including pregnancy, childbirth, breastfeeding, or medical conditions related to pregnancy, childbirth, or breastfeeding), age, ancestry, United States military or veteran status, color, religion, creed, marital or domestic partner status, medical condition, genetic information, national origin, citizenship status, low\-income status, or mental or physical disability so long as the essential functions of the job can be performed with or without reasonable accommodation, or any other protected category under federal, state, or local law. Learn more about your rights under Federal laws and supplemental language at Labor Laws Posters.
Salary Context
This $160K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Amentum, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($170K) sits 22% below the category median. Disclosed range: $160K to $180K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Amentum AI Hiring
Amentum has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Camp H M Smith, HI, US, Linthicum, MD, US, Fort Meade, MD, US. Compensation range: $160K - $255K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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