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About This Role
Job Description Summary
The Generative AI Solutions Engineer develops and supports data integrations, reporting, automation, and AI-enabled solutions with an emphasis on enterprise data and analytics.
The position is responsible for supporting the initial design, development and operationalization of enterprise AI agents that serve student success needs.
Working across the organization, the Generative AI Solutions Engineer, supports data governance, quality monitoring, metadata, lineage, tagging, and traceability to trusted sources.Key Responsibilities:
AI Solutions and AI-Enabled Analytics
- Develop, configure, test, and maintain student, advisor, and staff-facing AI agents using Microsoft Copilot Studio, Azure AI, Microsoft Fabric, and related technologies.
- Translate business use cases into technical requirements, solution designs, data requirements, prototypes, and testing plans.
- Build prompts, workflows, connectors, knowledge sources, integrations, and retrieval-augmented generation solutions using approved institutional data and documents.
- Monitor and troubleshoot enterprise solutions.
- Document AI architecture, data sources, testing results, safeguards, limitations, and responsible AI practices.
Data Quality, Classification, Metadata, and Lineage
- Develop and maintain data quality rules, validation processes, monitoring, and certification of trusted data assets and reports.
- Profile data and investigate errors; coordinate root-cause analysis and remediation.
- Apply and maintain data tags and approved definitions across enterprise platforms.
- Maintain metadata, automated harvesting, synchronization, and end-to-end lineage across systems.
Data Engineering and Operational Support
- Develop, test, monitor, and maintain data integration pipelines, Fabric assets, APIs, and related platform components using approved source-control, testing, deployment, security, and change-management practices.
- Onboard and integrate enterprise data sources, applications, files, APIs, and external services; build and validate extraction, transformation, reconciliation, loading, and automation processes.
- Document mappings, data flows, and operating procedures; troubleshoot pipeline failures, and performance issues.
Reporting and Analytics
- Develop and maintain Power BI dashboards, semantic models, analytical reports.
- Create dashboards that monitor AI performance, adoption, data quality, lineage, metadata, and pipeline reliability
Required Qualifications:
- Bachelor’s degree in computer science, information systems, data analytics, or a related field, or an equivalent combination of education and experience.
- At least 3-5 years of related experience in data integration, software development, analytics, automation, or reporting, and 2 years of experience with AI-enabled solutions.
- Working knowledge of SQL and experience with Python or another relevant programming or scripting language.
- Experience with one or more cloud data, integration, reporting, or automation platforms.
- Experience working with APIs, structured data, data validation, and technical troubleshooting.
- Strong problem-solving, documentation, communication, and collaboration skills, including the ability to translate defined business needs into practical solutions.
Preferred Qualifications:
- Experience with Microsoft Fabric, Azure, Power BI, Copilot Studio, or related Microsoft technologies.
- Experience developing or supporting AI agents, copilots, chatbots, workflow automation, or analytical solutions.
- Familiarity with source control, testing, deployment, data quality, privacy, security, or responsible AI practices.
- Experience working in higher education or with administrative systems such as student, ERP, CRM, or learning management platforms.
Work Environment:
- Typical office setting with extensive sitting and computer work.
- Ability to travel around campus for meetings and other work related duties.
Pay Transparency
At Bentley we offer a “total rewards” package designed to attract, motivate, retain, and reward our employees. This includes competitive compensation, robust benefits, career development and opportunities, generous paid time off, workplace flexibility and a positive, engaging employee experience. In alignment with the Massachusetts Pay Transparency Act and our commitment to equity and transparency, we’re sharing the salary grade range for this role. The grade range is based on market data and the scope of responsibilities. Final pay will depend on your experience, education, and other relevant qualifications.
Salary Grade Range:
$91,150 - $150,890 per year
If you have questions about the salary grade range or need additional information, please contact us at [email protected].
Bentley University requires references checks and may conduct other pre-employment screening.
DIVERSITY STATEMENT
Bentley University strives to create a campus community that welcomes the exchange of ideas, and fosters a culture that values differences and views them as a strength in our community.
Bentley University is an Equal Opportunity Employer, building strength through diversity. The University is committed to building a community of talented students, faculty and staff who reflect the diversity of global business. We strongly encourage applications from persons from underrepresented groups, individuals with disabilities, covered veterans and those with diverse experiences and backgrounds.
Salary Context
This $91K-$150K range is in the lower quartile 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 Bentley University, 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 $212,000 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 ($121K) sits 43% below the category median. Disclosed range: $91K to $150K.
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.
Bentley University AI Hiring
Bentley University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Waltham, MA, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 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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