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About This Role
Amentum is hiring a AI Technical Lead in Hanover, MD. The leader directs the AI engineering team, owns AI strategy and governance across the portfolio, and represents the company's AI capabilities to Government customers and industry partners. The role builds toward assuming the senior AI lead position for the sector.
Work Schedule:
5 days a week, 8 hours a day
Essential Responsibilities:
Responsible for defining the artificial intelligence strategy, overseeing technical execution, and guiding cross-functional teams to build scalable AI models and applications. They bridge the gap between business objectives and engineering by managing the AI lifecycle, selecting appropriate frameworks, and ensuring ethical data governance and compliance. Additionally, provide mentorship to AI team while communicating technical milestones to stakeholders to drive innovation and business growth.
Work Environment, Physical Demands, and Mental Demands:
Hybrid or modern office environment equipped with high-performance computing infrastructure and advanced cloud collaboration tools. Their day-to-day involves cross-functional collaboration, straddling the line between technical development teams and corporate executives to align data strategies with business goals. The environment can be fast-paced and high-pressure, requiring constant adaptation to evolving technologies.
Minimum Knowledge
- Large language model fundamentals, including transformer architecture, tokenization, embeddings, context windows, inference parameters, and common failure modes such as hallucination and prompt injection.
- Retrieval-augmented generation (RAG) design patterns, including chunking strategies, embedding model selection, vector and hybrid search, reranking, and retrieval evaluation.
- Model adaptation methods, including supervised fine-tuning, parameter-efficient fine-tuning (LoRA, QLoRA), and distillation, along with when each approach outperforms prompting or RAG alone.
- The open-weight model ecosystem (Llama, Mistral, Qwen, Gemma, or similar) and the licensing, provenance, and supply chain considerations for bringing models into Government environments.
- Security and compliance frameworks that govern AI in classified environments, including RMF and the ATO process, ICD 503, the NIST AI Risk Management Framework, and handling requirements for CUI and classified data.
Minimum Skills
- Demonstrated experience designing, building, and deploying at least one RAG application for real users, covering document ingestion, chunking, embedding selection, vector store configuration, and retrieval evaluation.
- Demonstrated experience fine-tuning or adapting open-weight LLMs (LoRA, QLoRA, or full supervised fine-tuning) and measuring results against a defined benchmark.
- Experience hosting and serving models directly on GPU infrastructure (vLLM, TGI, NVIDIA Triton, or similar), including GPU sizing and quantization tradeoffs.
- Experience delivering AI capabilities in disconnected or classified environments, including moving models, containers, and dependencies across the air gap and supporting the system through ATO.
- Experience building access controls retrieval pipelines so each user retrieves only content that matches their clearance, compartments, and need-to-know.
- Experience building evaluation and guardrails for LLM output, including accuracy testing, hallucination measurement, red teaming, and prompt injection defense.
- Experience building document ingestion pipelines for large, unstructured collections, including parsing, OCR, metadata tagging, and preserving classification markings.
- Proficiency in Python and at least one LLM orchestration framework (LangChain, LlamaIndex, Semantic Kernel, or similar), plus containerization with Docker and Kubernetes.
- Ability to explain AI concepts, tradeoffs, and limitations clearly to technical and nontechnical audiences, including Government leadership.
Minimum Abilities
- Ability to translate mission requirements into working AI architecture and choose the right approach (prompting, RAG, fine-tuning, or agents) for each problem.
- Ability to evaluate emerging models and tools quickly and recommend which merit adoption within customer security constraints.
- Ability to lead technical work across engineering, security, and mission teams.
- Ability to balance delivery speed with customer security, compliance, and governance requirements.
Team Leadership
- Experience leading a team of AI, software, or data engineers through full delivery cycles, including hiring, mentoring, performance management, and career development.
- Experience setting technical direction across several AI efforts at once and deciding where the team builds, buys, or partners.
- Experience owning AI governance for a program or organization, including model approval, usage policy, and alignment with customer security requirements.
- Experience building and running an AI Center of Excellence or community of practice that spread AI use across multiple programs.
- Experience supporting business development and capture, including solution architecture, technical volumes, BOEs, and orals.
- Experience representing the company with senior Government stakeholders and industry partners and turning mission needs into a funded AI roadmap.
- Experience building business cases and IRAD investment priorities that show measurable return.
Security Clearance Required:
- Active TS/SCI with POLY
Minimum Education:
- Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field.
- We will consider an equivalent combination of education and relevant experience in place of a degree.
Minimum Years of Experience:
- Minimum 10 years of professional experience in software engineering, machine learning engineering, data engineering, or Information Technology.
- At least 3 years leading technical teams.
- At least 2 years leading AI delivery for operational or business users.
Preferred Qualifications:
Above and Beyond AI Skills
- Designing agentic and multi-agent systems with human approval checkpoints, including tool integration through MCP or similar standards.
- AI security and adversarial machine learning, including red teaming, data poisoning, model extraction, and prompt injection, using the OWASP LLM Top 10 and MITRE ATLAS.
- Running small models at the tactical edge through distillation and quantization, for environments with limited or intermittent connectivity.
- Multimodal AI across imagery, full motion video, speech, and geospatial data.
- GraphRAG and knowledge graphs that pair structured relationships with retrieval for analytic work.
- Synthetic data generation for training and testing when real data is classified, scarce, or sensitive.
- AI data flows across classification levels through cross domain solutions.
- Planning AI cost and capacity, including GPU sizing, inference optimization, and token budgets.
- Current Federal and IC AI policy, including OMB, DoD CDAO, and ODNI guidance.
- Rolling out AI coding assistants and agents across an engineering team's development process.
- A public record such as publications, conference talks, patents, or open source contributions.
#javelin
Compensation Details:
$200,000 - $300,000
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:
10/12/2026 - 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 $200K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $200K to $300K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Amentum AI Hiring
Amentum has 4 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, Data Scientist. Positions span Remote, US, Lancaster, CA, US, Washington, DC, US. Compensation range: $180K - $300K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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