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About This Role
Base Pay Range:
$31\.57 \- $50\.76Job Description:
The Senior AI Solutions Engineer I is a Full Time, Regular position working Monday\-Friday day shift hours hybrid in Spokane, WA.
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Summary: This entry\-level position supports the design, development and deployment of GenAI\-powered tools and agents across our business operations. Working closely alongside our AI Solutions Engineer and Senior AI Solutions Engineer, this role will contribute hands\-on coding, AI integration, and workflow automation efforts—helping the organization streamline internal processes and boost productivity and quality.
General Description: The Junior AI Solutions Engineer must be able to work effectively and efficiently in a fast\-paced office environment in meeting continual deadlines. The Junior AI Solutions Engineer must be able to handle multiple priorities with constant interruptions. Organization is essential in order to meet deadlines. A sense of urgency, the ability to make good decisions, and the prioritization of tasks is necessary.
Essential Duties / Responsibilities: To perform this job successfully, an individual must be able to perform each essential duty. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- Assists in building and delivering AI agents and automation workflows tailored for medical imaging, business operations, and healthcare analytics, under the guidance of the AI Solutions Engineer and Senior AI Solutions Engineer.
- Foundational knowledge of REST API endpoints and ability to consume them in development tasks.
- Supports the development of generative AI models (e.g., large language models, diffusion models) and helps with integration of emerging AI APIs (Anthropic Claude, OpenAI GPT4, Google Gemini).
- Contributes to model deployment within production environments across cloud\-native and edge platforms, following established patterns and best practices.
- Assists with integrating AI\-driven solutions with EMR/EHR systems, PACS, clinical workflows, and modern data infrastructure.
- Participates in technical consulting activities, helping to communicate AI capabilities to clinical and operational stakeholders as directed.
- Supports the integration of AI tools into existing business systems and processes.
- Collaborates with process owners—with guidance from senior team members—to understand task logic and data flows.
- Follows established protocols for security, data privacy, and human\-in\-the\-loop review.
- Helps maintain and document AI systems to support stability and reproducibility.
- Applies clean coding standards and DevOps best practices as guided by senior team members.
- Other special projects and duties as assigned
General Duties/Responsibilities:
- Ability to maintain strict confidentiality within the Inland Imaging companies and Inland's customers.
- Honest, pleasant manner and good personal hygiene.
- Free of alcohol and drug abuse.
- Ability to access multiple worksites in a timely manner.
- Excellent communication and interpersonal skills.
- Detail oriented; ability to multi\-task; organized and able to work in a fast paced environment.
- Demonstrates self\-directed learning and participation in continuing education through professional journals, approved seminars, etc.
- Adheres to departmental standards and personnel policies by demonstrating professional demeanor in conduct and appearance.
- Follows company departmental standards and personnel policies by using good teamwork and communication skills to help identify concerns and solutions, assisting where needed to ensure a smoothly functioning department.
- Follows all Health and Safety policies and guidelines of Inland Imaging and its partners depending on work location
- Performs other duties as required by displaying team spirit and self\-growth, accepting and performing other projects and responsibilities, and requesting other projects and responsibilities.
- Attendance is required for this position.
- Rotates to other shifts and locations as needed.
Supervisory Responsibilities:
There is no supervisory responsibility in this position
Advocacy:
- Treats all clients with dignity and respect
- Provides excellent customer service
- Conforms to Joint Commission and HIPAA regulations
- Complies with PHI (Protected Health Information)
Demonstrates the Nuvodia Core Values:
Authenticate Connections, Ownership Mentality, Continuous Growth, and Enduring Results
Qualifications:
- Education: Bachelor's Degree in Software Engineering, AI, Data Science or equivalent experience.
- Experience: Zero to two (0–2\) years of related experience in Software Engineering or AI/ML development.
- Familiarity with generative AI models and exposure to AI APIs such as OpenAI, Anthropic, or similar platforms.
- Basic experience with CI/CD pipeline tools (GitHub Actions, Jenkins, Azure DevOps, or GitLab CI).
- Solid programming skills (Python required; JS, R, or similar a plus).
- Strong communication skills; able to engage with technical and non\-technical team members.
- Willingness to travel \~10% to client sites and conferences.
- Ability to work in a high energy, team environment.
- Licensure: Valid driver's license and vehicle insurance, or the ability to access multiple worksites in a timely manner.
- Computer Skills: Microsoft Office Suite, Advanced Excel, Word and PowerPoint, Workday, Smartsheet, etc.
- Drug Test: Eligible employees must be able to pass a post\-offer, pre\-employment drug test.
Nuvodia/Inland is an EEO employer...
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Salary Context
This $64K-$104K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Nuvodia, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($84K) sits 54% below the category median. Disclosed range: $64K to $104K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Nuvodia AI Hiring
Nuvodia has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Spokane, WA, US. Compensation range: $104K - $104K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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