Interested in this AI/ML Engineer role at PingWind Inc.?
Apply Now →About This Role
Location: Remote; travel may be required in support of meetings, training, presentations, or briefings with project stakeholders
Required Clearance: Ability to obtain and maintain the required VA background investigation/suitability determination
Certifications: Certification in one or more of the following: IAT III, IAM III or IASAE III certifications. IAT III/ IAM III / IASAE III certifications may be substituted for a relevant Bachelor’s degree or 4 years of relevant experience
Required Education: Master’s degree in Artificial Intelligence, Business Administration, Business Management, Cybersecurity, Computer Science, Information Systems, Information Assurance, Information Security, Information Resource Management, or related fields.
Required Experience: Minimum of 10 years of experience in relevant fields including IT security, data science, or software engineering, of which at least 5 years involve the design, development, or deployment of AI or machine learning systems in enterprise or Government environments A PhD in a related field (IT security, data science, or software engineering) may substitute for up to 5 years of the required experience
Description
PingWind is seeking a highly skilled and experienced Artificial Intelligence Engineer to join our dynamic team supporting a large Federal Government client. Apply AI, machine learning, data science, and software engineering expertise to develop and deploy secure AI/ML solutions for complex enterprise environments. The AI Engineer will help our government client responsibly integrate AI capabilities into enterprise security engineering and mission workflows.
Responsibilities
- Design, develop, train, test, and deploy AI/ML models and solutions.
- Collect, clean, analyze, and prepare datasets for AI/ML modeling.
- Identify AI-enabled automation opportunities that reduce manual effort and review cycles.
- Evaluate AI/ML solutions for performance, security, reliability, and enterprise suitability.
- Collaborate with data scientists, developers, cybersecurity engineers, architects, and domain experts.
- Document models, testing results, risks, controls, and technical recommendations.
- Apply AI/ML capabilities to enterprise security engineering, testing, and analysis.
Requirements
- Ability to obtain and maintain required VA background investigation/suitability and system access.
- Demonstrated experience building, training, and deploying AI/ML models in production environments.
- Experience applying AI solutions within large enterprise or Government environments.
Preferred Qualifications
- Federal/VA AI experience.
- MLOps and AI lifecycle experience.
- Responsible AI, model governance, or AI security experience.
About PingWind
PingWind has extensive experience in the fields of cybersecurity, development, IT infrastructure, supply chain management and other professional services such as system design and continuous improvement. PingWind is an SDVOSB with offices in Northern Virginia and Huntsville AL. www.PingWind.com
Our benefits include:
- Eleven Federal Holidays
- Paid Time Off accrued each pay period
- Parental Leave
- Three medical plan choices with generous employer contribution
- Dental and Vision Insurance
- Company paid Short-Term and Long-Term Disability
- Company paid Life and AD&D Insurance
- 401k with competitive matching and vesting schedule
- Continuing education assistance
- Short Term / Long Term Disability & Life Insurance
- Medical, Dependent Care and Commuter Flexible Spending Accounts
- Employee Assistance Program
- Wellness benefits include Calm Health app and WellHub gym subsidy (formerly GymPass)
- 529 College Savings Plan
- Legal Insurance
- Pet Insurance
Salary Range
121K - 169K
The pay range for this job is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) job responsibilities, education, certifications, experience, as well as internal equity mapping and alignment with market data, or other applicable laws.
Veterans are encouraged to apply
PingWind, Inc. does not discriminate in employment opportunities, terms, and conditions of employment, or practices on the basis of race, age, gender, religious or political beliefs, national origin or heritage, disability, sexual orientation, or any characteristic protected by law.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $121K-$169K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At PingWind Inc., 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $121K to $169K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
PingWind Inc. AI Hiring
PingWind Inc. has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Remote, US. Compensation range: $169K - $169K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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