Interested in this AI/ML Engineer role at Xenith Solutions?
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About This Role
Xenith Solutions is a small family focused business where we focus on taking care of our employees and customers equally. We are focused on serving Federal / Civilian, Defense and Intelligence organizations with superior service. If you want to be a part of a rapidly growing business with an exceptional culture, then you want to be a part of the Xenith Solutions family.
Xenith offers unmatched Benefits:
- 100% of Medical, Dental, and Vision benefits paid by employer
- FSA or HSA available
- Unlimited Paid Time Off (PTO)
- 401(k) matching (100% up to the first 4%) with NO vesting period
- Tuition / Certification / Training reimbursement
- Accident / Disability / Universal Life Insurance
- And much more…
Job Description:
Xenith Solutions is seeking an AI/ML Engineer with agile methodology experience to join our team in support of a Customs and Border Protection (CBP) contract. As a member of the team, you will support our prime contractor’s Agile Solution Factory (ASF), which is a passionate team of industry-leading personnel supporting the best practices in Agile Software Development. The ability to be adaptable and to work constructively with a technically diverse and geographically separated team is crucial. You should have worked with or have a strong interest in Agile software development practices and delivering deployable software in short sprints.
Serve as an Agile Scrum team member providing software development and maintenance for the delivery of releasable software in short sprint cycles. Responsible for activities associated with delivery of software solutions associated with customer-defined systems and software projects by working in close collaboration with software developers/engineers, stakeholders, and end users within Agile processes.
What You’ll get to do:
- Design, develop, and implement custom software solutions for a variety of AI/ML-related pilot projects and use cases including Google Gemini AI.
- Develop and work with APIs including RESTful services, authentication methods, data formats, and handling API errors.
- Use SQL and NoSQL database including to evaluate and choose the appropriate database type based on the application requirements;
- Integrate SQL and NoSQL databases within the same application, when appropriate; and apply advanced understanding of security best practices for both SQL and NoSQL databases.
- Act as a member of one or more prototyping teams supporting teammates and collaborating to deliver working software applications on short timelines.
- Analyze complex project-related problems and creating innovative solutions involving technology, methodology, tools, and solution components.
- Actively participate in agile delivery phases and ceremonies including release and sprint planning, artifact creation, sprint testing, demonstrations, and retrospectives
You Will Bring These Qualifications:
- Must be a U.S. Citizen with the ability to pass CBP background investigation, criteria include, but not limited to: + 3-year check for felony conviction + 1-year check for illegal drug use + 1-year check for misconduct such as theft or fraud
- + BA/BS and 7 years’ experience in a related field, or equivalent + Ability to analyze technically complex problems and develop and implement new and innovative solutions in a fast-moving and fluid software prototyping development environment. + Expertise in multiple software developer languages including, but not limited to: YAML, JSON, Python, Java, JavaScript and TypeScript. + Full Stack development experience including proficiency in both front-end (Angular) and back-end development, ability to handle all aspects of a web application from designing the user interface to managing the server and database. + Ability to work with Data Scientist teams to integrate applications with AI/ML, Big Data, or BI solutions. + Ability to work independently on a complex task with little direction and management oversight.
Desired Qualifications:
- Experience working with live data and video streams.
- Technical certifications such as AWS Certified Developer Associate.
- Experience with DevOps (CICD) solutions such as GitLab.
- Experience with AI/ML solutions or complex solutions for predictive analytics.
- Experience working in a geographically dispersed, remote team.
- Understanding of Data Science principles and best practices.
Xenith Solutions LLC is a Service-Disabled Veteran-Owned Small Business founded in 2019. We provide comprehensive, timely and relevant Solutions and Business Consulting support to our customers as a key partner. Our leadership brings over a century of combined experience in Defense and Civilian markets. Our employees possess experience in all aspects of solution development from requirements creation, development, test and evaluation, fielding, and sustainment. At the core of our offerings, we provide strategy and technology solutions, giving our customers valuable insights and thought leadership on the best application of information technology to drive business objectives.
Xenith focuses on solving complex business challenges facing our customers. Our “Success Through Achievement” work ethic means our customer receive quality solutions through our commitment. We pride ourselves on tackling some of the most difficult operational requirements our customers have – ensuring an appropriate match between the mission requirements, financials, schedule, and security.
EEO
Xenith Solutions provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status, amnesty, or status as a covered veteran in accordance with applicable federal, state and local laws.
EEO IS THE LAW
If you are an individual with a disability and would like to request a reasonable accommodation as part of the employment selection process, please contact Xenith Solutions.
E-Verify
As a Federal Contractor, Xenith Solutions is required to participate in the E-Verify Program to confirm eligibility to work in the United States.
Affirmative Action Plan
As a federal government contractor and based on Executive Orders and applicable laws and regulations, Xenith Solutions develops and maintains annual written Affirmative Action Plans and endeavors to hire and advance qualified minorities, females, individuals with disabilities, and protected veterans.
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Salary Context
This $115K-$165K range is below the median 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 Xenith 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $115K to $165K.
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.
Xenith Solutions AI Hiring
Xenith Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $165K - $165K.
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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