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About This Role
### About Us
AGE Solutions is a premier technology and professional services company, providing in\-depth consulting, advanced technology solutions, and essential services throughout the U.S. government, defense, and intelligence sectors. Prioritizing innovation and client\-focused solutions, we assist major agencies in addressing intricate issues and ensuring a more secure future.
AGE Solutions is seeking a highly motivated Artificial Intelligence (AI) Engineer to support our DoD customer's Emerging Technology Mission Assurance initiatives by researching, evaluating, engineering, and integrating Artificial Intelligence (AI), Machine Learning (ML), Generative AI, and other emerging technologies that strengthen Department of Defense cybersecurity and enterprise capabilities.
Working directly with our customer's engineers, industry partners, and government stakeholders, the successful candidate will conduct technology research, support technology assessments and proof\-of\-concept activities, evaluate commercial and government AI solutions, and provide engineering recommendations that inform enterprise architecture, cybersecurity modernization, and future technology adoption across the DoD Information Network (DoDIN).
Responsibilities Include:
- Research, evaluate, and assess emerging technologies, including Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLMs), Generative AI, cloud computing, cybersecurity, identity management, networking, and advanced computing platforms.
- Analyze commercial and government technology solutions and provide technical recommendations supporting DISA modernization initiatives.
- Design, develop, and execute technology evaluations, laboratory testing, pilot programs, and proof\-of\-concept demonstrations.
- Develop technology evaluation plans, operational use cases, success criteria, performance metrics, and technical assessment reports.
- Develop AI prototypes and assist with transitioning successful capabilities into operational environments.
- Evaluate AI model performance, scalability, security, and operational effectiveness.
- Apply systems engineering principles throughout technology evaluation, integration, testing, and transition activities.
- Develop engineering documentation, including architecture diagrams, technical analyses, white papers, CONOPS, and engineering recommendations.
- Support DISA integration with commercial cloud providers and cloud\-hosted AI capabilities.
- Evaluate AI workloads across hybrid, on\-premises, and multi\-cloud environments.
- Collaborate with DISA organizations, DoD agencies, Combatant Commands, industry partners, and other stakeholders to evaluate emerging technologies.
- Present technical findings, recommendations, and executive briefings to government leadership and Integrated Product Teams (IPTs).
- Support strategic technology planning, technology roadmaps, business case analyses, and technology migration strategies.
Required Skills, Qualifications, and Experience:
- Experience:
+ 10 Years of experience
- 5\+ years supporting Artificial Intelligence, Machine Learning, Systems Engineering, Emerging Technologies, Cybersecurity, or Cloud Engineering
- Education:
+ Bachelor's degree in computer science, Artificial Intelligence, Systems Engineering, Cybersecurity, Information Technology, or a related technical field (or equivalent experience).
- Security Clearance:
+ Must have and maintain a current DoD Top Secret Clearance.
- Certifications:
+ Current DoD 8140 baseline certification (e.g., Security\+ CE, CEH)
- Highly Desired: Microsoft Azure AI Engineer Associate or Azure AI Fundamentals AWS Certified AI Practitioner or AWS Machine Learning Engineer
- Experience with Artificial Intelligence, Machine Learning, Large Language Models (LLMs), Natural Language Processing (NLP), Computer Vision, or Generative AI technologies.
- Experience conducting technology research, evaluations, proof\-of\-concepts, or pilot projects.
- Knowledge of systems engineering principles and technology lifecycle management.
- Experience with cloud computing platforms and AI\-enabled cloud services.
- Strong analytical, technical writing, and presentation skills.
- Ability to develop technical documentation, reports, and executive briefings.
- Strong communication and collaboration skills with government and technical stakeholders.
Preferred Qualifications:
- Experience supporting DISA, DoD, Federal Government, or Intelligence Community technology modernization programs.
- Experience with AI governance, Responsible AI, and AI security within government or regulated environments.
- Experience with commercial cloud platforms such as Microsoft Azure or Amazon Web Services (AWS).
- Familiarity with Zero Trust Architecture, NIST Risk Management Framework (RMF), and DoD cybersecurity policies.
- Experience supporting enterprise architecture, technology roadmaps, and modernization initiatives.
- Experience briefing senior government leadership and executive stakeholders.
- Experience with DevSecOps, automation, containerization, or Kubernetes.
- Knowledge of DISA Enterprise Integration and Innovation Center (EIIC), Risk \- Management Executive (RME), or emerging technology assessment initiatives.
- Active Top Secret clearance with SCI eligibility preferred.
Work Environment and Physical Demand:
- This work will be conducted in an office environment. Must be able to sit for prolonged periods of time.
- Location and Schedule:
+ Must be within commuting distance of Fort Meade, MD and able to work on\-site on a hybrid schedule.
- 1 day/week onsite required with additional days onsite if the project requires.
The projected salary range for this position is $155,000\+ annually. Final compensation will be determined based on factors including years of relevant experience, active security clearance level, certifications, technical skillset, contract requirements, and overall qualifications.
At AGE Solutions, we reward performance, invest in growth, and share success. Our benefits support the whole person, professionally, financially, and personally.
- 26 Days Paid Leave: Includes vacation, sick, personal time, and holidays. You choose how to use it.
- Performance Bonuses: Performance bonuses are awarded based on individual contributions and company\-wide results, aligning recognition with impact.
- 401(k) with Match: We match 3% of your contributions with immediate vesting.
- Financial Protection: Company\-paid life insurance up to $300K and options for additional coverage for you and your dependents.
- Health Benefits: Multiple medical plans, dental, vision, FSA and HSA options to fit your needs.
- Parental Leave: 15 days of fully paid leave for new parents, because family matters.
- Military Differential Pay: We bridge the gap for employees on active duty, so they don't take a financial hit while serving.
- Professional Growth: Paid training and certifications, tuition reimbursement, and the tools and tech to get the job done right.
- Shared Success: In the event of a company sale, our CEO has committed to returning 80% of net proceeds to employees. This ensures our team shares in the long term value they help create.
At AGE, you'll do work that matters, supported by a company that delivers for its people.
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 AGE Solutions, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
AGE Solutions AI Hiring
AGE Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Meade, MD, US.
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.
Frequently Asked Questions
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