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About This Role
Based in Northern Virginia, Axiologic Solutions LLC, has an opportunity for you to become part of our high-quality team that delivers innovative solutions to key federal clients. We are currently seeking a Product Managment GenAI Product Integration SME to support our growing team.
Position Overview:
This is a product manager enabler role to integrate Generative Artificial Intelligence Mission Application capable of operationalizing Maven GenAI within the joint military environment. This individual is expected to manage product engagements and deliver prompt engineering workflows against strategic objectives that enable autonomy into military planning; tactics, techniques and procedures (TTP).
Key Responsibilities:
GenAI Subject Matter Experts are integral to ensuring the success of the program as technology rapidly advances. They are key players in translating user needs into product strategies and supporting the NGA AI/ML Program in the development and deployment of agentic AI and decision support. As a member of the NGA AI/ML Program team, the selected candidate will have the following roles and responsibilities:
- Use GenAI to analyze large amounts of data to find patterns and recommend solutions that will improve intelligence workflows. Work will require working in teams to solve large, complex problems to achieve interrelated and interdependent objectives.
- Provide strategic insight and advice concerning the direction and applicability of industry-standard solutions.
- Offer specialized expertise to facilitate the development of comprehensive methods for defining present and future strategic GenAI product milestones.
- Monitor all facets of product performance, and assist in the formulation of long-term plans.
- Utilize industry-specific knowledge to evaluate an organization's operational and functional baseline.
- Participate actively in account strategy sessions, strategic assessments, and design reviews to validate enterprise approaches and associated work products.
- Stay abreast of developments within their field of expertise and apply them to the client environment, encompassing emerging technologies, lessons learned, best practices, and assessment methodologies.
- Engage with users and customers to discern and comprehend their underlying needs.
- Collaborate with both internal and external stakeholders, including product managers, to formulate a product vision and roadmap that aligns with the NGA AI/ML Program's objectives.
- Work in close coordination with software engineering teams to fulfill user needs and construct user experiences that meet the standards expected by the Intelligence Community, Department of Defense (DoD), and government workforce.
- Support the entire product development lifecycle, from conceptualization and design to implementation and evaluation, particularly for moderately complex product development efforts.
- Effectively communicate, educate, and advocate the vision and goals of new GenAI/Agentic AI products, both internally and externally.
Qualifications:
- TS/SCI current - Counterintelligence Poly preferred but required at time of join.
- Experience in briefing leadership, senior policymakers, IC members, and other intelligence consumers
- Capable of coordinating and collaborating with mission partners and senior internal/external stakeholders to support program implementation and integration
- Experience developing and maintaining performance measures for capabilities and programs
- Understands Generative AI tools and interfaces and has user facing experience in prompt engineering to inform and increase product adoption
- Strong communication skills, with the ability to clearly articulate product goals, updates, and expectations to both internal teams and external stakeholders, ensuring alignment and collaboration
- Experience collaborating with internal and external stakeholders to collect, validate, and prioritize requirements, develop plans, and execute required actions in support of operational goals
- Experience working in joint military environment; knowledge of the Joint Targeting Cycle to include Target System Analysis, Advance Target Development, Target List Management, and Battle Damage Assessment
- Understanding of Tasking, Collection, Processing, Exploitation and Dissemination (TCPED) Processes and Policy
- Understands Artificial Intelligence products and services and how to assess and operationalize products into applicable workflow across the joint military environment
- Must understand GenAI integrations and API service connections
Required:
- A Bachelor's degree in a relevant field such as Computer Science, Information Technology, Business, Communications or a related discipline.
- Critical thinker that is comfortable supporting collaborative problem solving in a team environment
- Analytical and problem-solving skills, with the ability to use data and metrics to drive product decisions.
- 6+ months of experience in AI space
- Adaptive Learning & AI Curiosity: The candidate should demonstrate a proven track record of proactively seeking out new knowledge, adapting to the ever-evolving landscape of artificial intelligence, and applying fresh insights to their work. Familiarity with the latest AI research, participation in relevant courses or workshops, and a willingness to pivot in response to new technological advancements are essential.
Desired:
- Relevant certifications or additional education in Product Management, Generative Artificial Intelligence, Agentic AI, or a related field are a plus.
- Lead high-performing teams delivering IT solutions.
- AI/ML Familiarity, an understanding of AI/ML concepts, algorithms, and current trends.
- Cross-functional collaboration, experience working with diverse teams, such as data scientists, engineers, vendors, and military professionals.
- Experience working in a joint military environment.
Salary Range - $133,250 – $276,750
The salary range is provided in good faith and is based on several job-related factors, including, but not limited to, the role and associated responsibilities, Federal Government contract labor category requirements, contract wage determinations when applicable, clearance requirements, work location, relevant experience, education, certifications, specialized skills, internal equity, and approved funding.
For proposal-contingent positions, the listed range is an anticipated good-faith range based on current proposal assumptions. Final compensation may vary based on contract award, finalized customer requirements, approved funding, labor category alignment, and candidate qualifications.
Axiologic Solutions invests in its employees beyond compensation. Depending on position and eligibility, benefits may include medical, dental, and vision insurance, life insurance, short-term and long-term disability, paid time off, holiday pay, 401(k) with company match, tuition assistance for courses and certifications, professional development opportunities, fitness and wellness support, electronic stipend, and other optional benefit elections.
Applicants with a physical or mental disability who require a reasonable accommodation for any part of the application or hiring process may email their request to [email protected] or call 571-295-4990. Determinations on requests for reasonable accommodation will be made on a case-by-case basis.
Axiologic Solutions and its subsidiaries are an Equal Opportunity Employer and federal government contractor. We do not discriminate against any employee or applicant for employment as protected by applicable federal, state, or local laws.
Salary Context
This $133K-$276K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Axiologic 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $133K to $276K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Axiologic Solutions AI Hiring
Axiologic Solutions has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Herndon, VA, US, Springfield, VA, US. Compensation range: $186K - $276K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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