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About This Role
Since 1998, Businessolver has delivered market\-changing benefits technology and services supported by an intrinsic responsiveness to client needs. The company creates client programs that maximize benefits program investment, minimize risk exposure, and engage employees with easy\-to\-use solutions and communication tools to assist them in making wise and cost\-efficient benefits selections. Founded by HR professionals, Businessolver's unwavering service\-oriented culture and secure SaaS platform provide measurable success in its mission to provide complete client delight.
#### \*\*Please be aware of recruitment scams. Businessolver does not make job offers outside of our official hiring process or request payment or sensitive personal information. You will never receive an offer of employment without meeting a hiring authority and having a "live" and face\-to\-face conversation.\*\*
Job Summary:
The Principal Test Engineer comes with proven success applying AI to test automation and quality assurance. This individual is responsible for improving product quality, reducing dependency on manual processes and increasing overall testing efficiency. They help to define and advance our enterprise testing strategy, maintain and evolve the UI automation framework, support load and performance testing and ensure test coverage aligns to system architecture, product risk, and delivery priorities. The Principal Test Engineer develops agent\-driven manual and automated tests and guides the adoption of agentic testing practices utilizing best in class AI and testing technologies across engineering, product, and architecture teams to increase throughout while elevating quality.
The Gig:
- Develop AI Agents that perform manual and automated testing to support rapid delivery of code changes.
- Drive the development and adoption of agent\-driven manual and automated testing practices to improve test creation, execution efficiency, coverage, and defect detection.
- Define and maintain the overall testing strategy across unit, integration, API, UI, regression, exploratory, performance, and risk\-based testing.
- Own the direction, maintenance, and continuous improvement of the UI automation framework, with emphasis on reliability, scalability, reuse, speed, and long\-term maintainability.
- Lead the technical approach for performance and load testing, including test design, tooling direction, coverage priorities, result analysis, and recommendations for system improvement.
- Build a strong understanding of system architecture, application workflows, integrations, and technical dependencies, and align testing strategy to the areas of highest business and technical risk.
- Establish, document, and refine testing standards, engineering best practices, and quality guardrails that improve consistency and reduce defect escape.
- Provide hands\-on technical leadership in test design, framework enhancement, troubleshooting, and resolution of complex quality and automation challenges.
- Mentor members of the quality team in sound testing practices, including stronger use of unit and integration testing.
- Partner with software engineers, architects, product teams, and other stakeholders to influence quality strategy early in solution design and throughout delivery.
- Evaluate and recommend testing tools, frameworks, and methodologies that improve productivity, test signal quality, and long\-term supportability.
- Review test coverage, quality risks, and validation approaches for new features and platform changes, and communicate clear recommendations to technical and non\-technical stakeholders.
- Promote continuous improvement.
- Comply with all standards and policies.
- All other duties as required.
What you need to make the cut:
- Bachelor degree in computer science, engineering or a related field or equivalent practical experience.
- 8\+ years of experience in quality assurance, software engineering, or a related field, with a strong focus on automated and manual testing strategies in high\-volume environments.
- Proven success using AI\-assisted and agent\-driven testing to improve coverage, efficiency, and quality outcomes.
- Experience developing and executing enterprise test strategies across unit, integration, API, UI, regression, exploratory, performance, and risk\-based testing.
- Strong expertise with automation frameworks and tools including TestNG, JUnit, Selenium, API testing tools, and related quality technologies.
- Experience with performance and load testing, including test design, analysis, and actionable recommendations.
- Strong understanding of system architecture, integrations, and technical dependencies, with the ability to align testing to business and technical risk.
- Ability to establish testing standards, best practices, and quality guardrails that improve consistency and reduce defects.
- Experience mentoring engineers in effective unit and integration testing practices.
- Hands\-on technical leadership in test design, framework strategy, troubleshooting, and automation improvements.
- Ability to collaborate with engineering, architecture, product, and business teams to influence quality throughout delivery.
- Excellent written and verbal communication skills with technical and non\-technical audiences.
- Experience with Git and collaborative development practices.
Preferred
- Experience supporting quality engineering in SaaS, cloud, or distributed application environments.
- Proficiency in Java, Python, XML, or similar technologies used in test automation.
- Knowledge of object\-oriented programming and automation framework design.
- Experience evaluating and implementing new testing tools, frameworks, and practices.
- Familiarity with API testing, performance testing, and broader quality governance.
*The pay range for this position is 93K to 140K per year (pay to be determined by the applicant's education, experience, knowledge, skills, and abilities, as well as internal equity and alignment with market data).*
*This role is eligible to participate in the quarterly bonus incentive plan.*
*Other Compensation: If this position is full\-time or part\-time benefit eligible, you will receive a comprehensive benefits package which can be viewed here: https://businessolver.foleon.com/bsc/job\-board\-businessolver\-virtual\-benefits\-guide/*
Dear Applicant.
At Businessolver, we take our responsibility to protect our clients, employees, and company seriously and that begins with the hiring process.
Our approach is thoughtful and thorough. We've built a multi\-layered screening process designed to identify top talent and ensure the integrity of every hire. This includes quickly filtering out individuals who may attempt to misrepresent themselves or act in bad faith.
We also partner with trusted, best\-in\-class providers to conduct background checks, verify identities, and confirm references. These steps aren't just about compliance, they're about ensuring fairness, safety, and trust for everyone involved.
Put simply: we will always confirm that you are who you say you are. It's just one of the many ways we uphold the standards that matter most, to you, to us, and to the people we serve.
With heart,
The Businessolver Recruiting Team
*Businessolver is committed to maintaining an environment that protects client data. We train our employees to maintain leading class security practices and expect all employees to adhere to policy, procedures and controls.*
*(Applicable to all roles at an AVP, DIR, VP, Head Of or SVP and above level):*
*Serve as a security contact for the business unit. Responsible for driving adoption and compliance with information security and privacy practices. Serve as a liaison with the information security team on security and privacy matters.*
Equal Opportunity at Businessolver:
Businessolver is an Affirmative Action and Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
*\#LI\-Remote*
Salary Context
This $93K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Businessolver, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($116K) sits 46% below the category median. Disclosed range: $93K to $140K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Businessolver AI Hiring
Businessolver has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $140K - $140K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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