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About This Role
Amentum’s Engineering \& Technology (E\&T) organization is expanding its capacity to pursue increasingly complex Department of War (DoW) opportunities across a broad portfolio. Historically, dedicated solution architects have focused on large pursuits; however, smaller and mid\-sized bids now exhibit comparable technical and operational complexity and demand enhanced solutioning support during capture and proposal development.
The AI\-Enabled Solution Architect – Capture \& Proposal Support is an entry\-level architecture role, reporting to the Lead Solution Architect (LSA), and serving as a shared resource across capture teams. This position focuses on using advanced AI tools and techniques to help shape compliant, compelling, and competitive technical solutions and translate them into proposal\-ready artifacts. The role is pre\-award only (capture/proposal) with no direct responsibility for post\-award execution.
Working closely with capture managers, proposal managers, technical SMEs, and operations/program managers, this architect will leverage internal enterprise AI platforms (e.g., ChatGPT.gov, AwardedAI) and commercial tools (e.g., Claude) to accelerate solution development, improve consistency and innovation, and ultimately increase Probability of Win (PWin) across Amentum’s DoW pursuits.
Key Responsibilities
Capture and Proposal Solution Support
- Support live DoD captures and proposals by developing concept\-level technical solutions that directly respond to solicitation requirements, evaluation criteria, and customer constraints, under the guidance of the Lead Solution Architect.
- Translate customer requirements, capabilities narratives, and technical baselines into proposal\-ready artifacts (e.g., solution descriptions, features/benefits tables, compliance mappings, discriminators) using AI\-assisted analysis and content generation.
- Assist in creating and refining solution architectures, workflows, and patterns that can be reused across multiple bids, focusing on clarity, compliance, and competitive differentiation rather than detailed implementation design.
- Contribute to technical and management volumes (and relevant sections in other volumes) by drafting and refining content that describes how the proposed solutions meet mission needs, reduce risk, and deliver measurable value.
AI\-Enabled Solutioning and Tools Utilization
- Use internal AI platforms (such as ChatGPT.gov and AwardedAI) and commercial tools (such as Claude) to:
- Analyze RFPs, RFIs, and related documents to identify requirements, risks, and opportunities.
- Generate and refine solution concepts, narratives, and supporting materials.
- Develop and maintain repeatable prompt patterns and AI workflows that improve speed and quality of solution development.
- Apply sound prompt engineering practices to guide AI tools toward accurate, relevant, and compliant outputs, with a focus on defense missions and federal contracting constraints.
- Curate and organize AI\-generated content to ensure it is technically coherent, consistent with capture strategy, and aligned with the Lead Solution Architect’s direction.
PWin and Competitive Differentiation
- Work with capture managers and proposal managers to ensure AI\-enabled solution concepts directly support win themes, strengths, and discriminators.
- Assist in identifying where AI\-assisted analysis reveals potential solution enhancements, efficiencies, or innovative features that can be incorporated into the proposal to improve PWin.
- Support color team reviews (e.g., Pink, Red, Gold) by rapidly iterating and refining solution narratives and artifacts using AI tools, based on reviewer feedback and evolving capture strategy.
Collaboration and Stakeholder Engagement
- Collaborate closely with:
- Lead Solution Architect (primary supervisor and technical mentor)
- Capture managers (solution strategy and win themes)
- Proposal managers (volume structure, schedule, and compliance)
- Technical SMEs and engineering leads (technical accuracy and feasibility)
- Operations and program managers (past performance, operational realism, and transition/implementation perspectives)
- Operate as a shared resource across multiple pursuits, balancing workload and priorities as directed by the Lead Solution Architect and capture/proposal leadership.
AI Advocacy, Enablement, and Safe Use
- Serve as a day\-to\-day advocate for the safe and effective use of AI in capture and proposal activities, modeling best practices for colleagues.
- Support AI pilots and internal experimentation related to proposal development and solutioning, including documenting lessons learned and recommended practices.
- Informally teach and coach capture/proposal team members on how to use AI tools effectively (e.g., basic prompt strategies, use cases, and limitations) while emphasizing appropriate oversight, quality assurance, and data protection.
- Promote responsible, secure, and compliant exploitation of AI tools in alignment with Amentum policies, customer requirements, and applicable regulations.
Required Qualifications
- Experience: Minimum of 10 years of experience supporting DoW with direct exposure to government mission support environments.
- Domain Knowledge: Strong understanding of DoW missions, operational contexts, and typical solution constructs (e.g., systems integration, logistics and sustainment, training and simulation, C5ISR, or other relevant defense domains). Familiarity with the structure and content of federal proposals, including typical technical and management volume constructs and evaluation criteria.
- AI Tools and Practices: Demonstrated proficiency using commercial AI products (e.g., ChatGPT, Claude, or similar tools) for professional tasks such as analysis, drafting, solution exploration, and content refinement. Experience designing effective AI prompts and repeatable prompt patterns that produce consistent, high\-quality outputs in support of solution development and proposal writing.
- Skills and Competencies: Strong analytical skills with the ability to rapidly assimilate RFP requirements, technical baselines, and stakeholder input, and synthesize them into coherent solution narratives. Excellent written communication skills, particularly in formal, structured, and customer\-facing proposal content. Ability to work collaboratively in a fast\-paced, deadline\-driven environment with multiple concurrent pursuits. High attention to detail, especially in ensuring compliance, consistency, and logical traceability from requirements to solution features.
- Education: High school diploma or equivalent required.
- Eligibility: Must be a U.S. citizen and eligible to obtain and maintain a security clearance as required by assigned projects. (Specific clearance level to be determined based on contract/customer needs.)
- Schedule: Must be able to support proposal timelines, including surge periods and non\-standard hours when necessary to meet submission deadlines.
Preferred Qualifications
- Bachelor’s degree in a related field (e.g., engineering, computer science, data/AI, business, or a defense\-related discipline) is preferred but not required.
- Familiarity with internal or enterprise\-grade AI platforms (e.g., government\-specific or secure AI environments such as ChatGPT.gov, AwardedAI, or equivalents).
- Exposure to common AI solution concepts (e.g., generative AI, retrieval\-augmented generation, prompt engineering, and AI\-assisted analytics) from a conceptual/architectural perspective.
- Experience working within structured capture/proposal processes (e.g., Shipley\-based methods, gated reviews, color teams).
- Experience collaborating with cross\-functional teams that include technical SMEs, program managers, and business development professionals.
Working Conditions
- Location: Remote within CONUS; occasional travel may be required for in\-person team working sessions, internal workshops, or proposal orals (estimated \<20%).
- Schedule: Must be able to support proposal timelines, including surge periods and non\-standard hours when necessary to meet submission deadlines.
- Reporting: Reports to the Lead Solution Architect within Amentum E\&T; operates as a shared resource supporting multiple capture and proposal teams.
Why This Role Matters
- This AI\-Enabled Solution Architect role is specifically designed to augment capture teams and increase organizational capacity to address complex, “smaller” bids that might otherwise be underserved, particularly within the DoW portfolio. By applying AI tools to solution shaping and proposal development, this position improves the quality, consistency, and innovation of Amentum’s proposals and directly supports the strategic objective of increasing PWin across the full spectrum of pursuits, not just the largest deals.
Compensation Details:
$140,000
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:
08/06/2026 \- 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.
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 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000.
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.
Amentum AI Hiring
Amentum has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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.
Frequently Asked Questions
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