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About This Role
Position Summary:The Senior AI Solutions Analyst is a hands‑on technical role responsible for designing, building, and deploying AI‑driven tools and intelligent agents that improve business processes and operational efficiency. This individual will use their expertise in Python, machine learning, automation frameworks, and workflow engineering to create practical, high‑impact solutions. In addition to development work, the Senior AI Analyst will help shape the customer’s AI strategy—identifying high‑value use cases, creating implementation roadmaps, and guiding adoption across the organization. This position is hybrid with on‑site engagement depending on mission needs. Essential Job Functions:
- AI Development & Engineering + Build custom AI agents, automation scripts, and intelligent workflows using Python and modern AI frameworks (e.g., Gemini, SpaceX AI, OpenAI/Azure APIs). + Develop and train machine learning or LLM‑based models for business process improvement. + Integrate AI tools into existing systems, data sources, and DOW workflows. + Create reusable components, libraries, and templates to accelerate future AI development. + Conduct data preparation, preprocessing, feature engineering, and evaluation of model performance.
- AI Strategy & Roadmapping + Identify opportunities where AI can significantly impact efficiency, quality, or customer outcomes. + Develop AI adoption roadmaps, including milestones, dependencies, staffing, data needs, and risk considerations. + Advise leadership on emerging technologies, feasibility, and best practices. + Support development of responsible AI guidelines aligned with organizational requirements.
- Process Improvement & Implementation + Analyze DOW processes to understand pain points and automation opportunities. + Work directly with business units to gather requirements, build prototypes, and deploy production-ready solutions. + Create documentation, training materials, and user guides for AI tools. + Monitor solution performance and iterate improvements over time.
- Collaboration & Support + Work closely with IT, data teams, and operational leaders to deploy solutions that meet technical and compliance standards. + Support change management by helping teams adopt new AI-enabled workflows. + Provide mentoring to junior analysts or developers on AI tools and best practices.
Key Competencies:
- Technical execution and hands‑on development.
- Strategic thinking paired with practical engineering skills.
- Strong communication and requirement‑gathering capability.
- Ability to work independently and lead small initiatives.
- Innovation mindset and continuous learning.
Skills and Qualification Required:
- U.S. Citizenship required.
- Clearance eligibility required
- Bachelor’s degree in Data Science, Computer Science, Engineering, or related field (or equivalent experience).
- 5–10+ years in data science, machine learning, data engineering, or advanced analytics.
- Experience supporting DoD programs, warfighter missions, or secure data platforms.
- Strong proficiency in Python, including experience building automation tools and integrating APIs.
- Hands‑on experience with AI and machine learning libraries (e.g., PyTorch, TensorFlow, scikit‑learn) and LLM toolkits.
- Experience designing and deploying AI agents or workflow automation solutions.
- Understanding of enterprise data environments and DOW workflows.
- Ability to translate operational needs into technical requirements and deliver practical solutions.
- Strong analytical skills, problem‑solving ability, and attention to detail.
Skills and Qualifications Preferred:
- Experience with generative AI frameworks, vector databases, orchestration tools, or MLOps pipelines.
- Familiarity with cloud environments such as Azure, AWS, or GCP.
- Experience in government contracting or regulated environments.
- Knowledge of NIST AI RMF, responsible AI practices, or privacy/security considerations.
About Us: At HunaTek, we build teams of people from all backgrounds with varying levels of experience, knowing firsthand that diversity of thought will strengthen our ability to deliver for our customers. We work hand in hand with Federal civilian and military staff, pulling together to further the interests of our nation and home and abroad. Whenever possible, we provide opportunities for our employees to learn new skills, obtain certifications, attend industry events, and have some fun together.
Our Benefits:
We offer a comprehensive benefits package designed to make sure our employees and their families have access to good health care, are insured against catastrophic health events, can put money aside for retirement and are able to maintain a healthy work-life balance. These benefits include:
- Comprehensive medical, dental and vision
- Long-term and short-term disability insurance and term life insurance
- 401(K) with safe harbor contribution
- Paid time off and 11 paid holidays
- Tuition and career development assistance
- A selection of voluntary benefits
This job description will be reviewed periodically as duties and responsibilities change with business necessity. Essential and marginal job functions are subject to modification. Equal opportunity employer as to all protected groups, including protected veterans and individuals with disabilities.
Salary Context
This $170K-$195K 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 HunaTek Government 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($182K) sits 14% below the category median. Disclosed range: $170K to $195K.
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.
HunaTek Government Solutions AI Hiring
HunaTek Government Solutions has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Quantico, VA, US. Compensation range: $185K - $195K.
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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