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About This Role
The AI Solutions Engineer, Advancement will design, build, and scale AI-enabled solutions that transform how Advancement teams work, make decisions, and engage alumni, donors, parents, and other constituents. Specifically responsible for developing and supporting AI solutions that improve fundraising effectiveness, donor and alumni engagement, prospect strategy, reporting, stewardship, and operational efficiency; and working closely with Advancement staff, IT, Analytics, and CRM/data teams and other campus partners to translate business needs into secure, scalable, and practical AI solutions.
Babson College is advancing the responsible use of AI to strengthen constituent engagement, improve decision-making, and streamline the systems and processes that support fundraising, alumni relations, and Advancement strategy. This work will also help staff make better use of data, reduce manual work, and improve the systems that support fundraising and alumni relations, including Salesforce, Microsoft, and related tools.WHAT YOU WILL DO
- Design, develop, deploy, and continuously improve enterprise-grade AI solutions that leverage Advancement CRM data, Salesforce, enterprise AI platforms, reporting environments, APIs, and related technologies to support fundraising, alumni engagement, prospect strategy, and operational efficiency.
- Develop and maintain an Advancement AI roadmap by identifying, evaluating, and prioritizing high-value use cases that align with fundraising, alumni engagement, stewardship, campaign objectives, and institutional strategy. Define and track business outcomes of AI initiatives, including productivity gains, user adoption, process improvements, fundraising effectiveness, service quality, and return on investment.
- Collaborate with Advancement colleagues, fundraisers, Advancement Services, IT, Analytics, CRM administrators, data governance partners, and end users to design, test, deploy, and continuously improve AI-enabled services and workflows. Design and deploy enterprise-wide and department-specific AI agents and agentic workflows that can retrieve information, use approved tools and data, recommend actions, and automate defined business processes.
- Partner directly with Advancement stakeholders to gather requirements, prototype and test solutions, train users, document workflows, drive adoption, and connect business needs with technical capabilities and responsible AI practices.
- Translate Advancement priorities into practical AI solutions, including donor briefings, interaction summaries, next-best-action recommendations, prospect research, campaign and pipeline insights, stewardship support, internal knowledge retrieval, and workflow automation.
- Build and maintain AI agent, agentic workflows, retrieval-augmented generation solutions, prompts, integrations, APIs, evaluation frameworks, and monitoring practices that ensure quality, accuracy, reliability, transparency, and usability.
- Help Advancement staff adopt AI tools and redesign individual and team workflows to improve productivity, decision-making, and effectiveness.
- Embed privacy, role-based access, security, data governance, responsible AI, human oversight, bias mitigation, and institutional policy requirements into solution design and deployment.
- Establish and apply evaluation frameworks for AI solutions, including accuracy, groundedness, reliability, latency, security, cost, user adoption, business impact, return on investment, and appropriate human oversight.
- Assume additional responsibilities as required
YOUR TEAM WILL INCLUDE
N/A
WHAT EDUCATION AND SKILLS YOU WILL NEED
Bachelor’s Degree in Computer Science, Data Science, Analytics, Information Systems, or a related field preferred; equivalent applied experience in AI solutions, data systems, or Advancement technology may be considered.
4+ years of experience developing, integrating, or deploying enterprise software, automation, analytics, data, or AI solutions, including hands-on experience building generative AI applications.
Proficiency in SQL and Python programming required.
Experience working with CRM, constituent, customer, donor, prospect, campaign, or engagement data required.
Must have hands-on experience with generative AI application development, prompt engineering, retrieval-augmented generation, AI agents or agentic workflows, API integrations, workflow automation, testing, debugging, and solution documentation.
Strong problem-solving and product-thinking skills, with the ability to rapidly prototype, test with users, productionize solutions, measure outcomes, and iterate based on feedback.
Experience developing and deploying cloud-based AI services using platforms such as Microsoft Azure, Microsoft Fabric, Salesforce, or comparable enterprise cloud and AI platforms, including secure data pipelines and enterprise deployment practices.
Ability to work collaboratively with technical and nontechnical stakeholders, translate business requirements into solutions, communicate clearly with Advancement users, and support change management and adoption.
Must have experience with AI backend services, enterprise-grade features, data security, model evaluation, responsible AI practices, and an enthusiasm for leveraging AI to improve Advancement outcomes and staff effectiveness.
Envisions and proposes new methods to improve Advancement operations, fundraising productivity, reporting, prospect strategy, donor engagement, and stewardship; takes thoughtful risks; and advances new and ongoing AI-enabled initiatives to achieve measurable results.
Anticipates and embraces change; demonstrates willingness to achieve, acquire, and utilize new skills and challenging tasks; and is flexible in changing conditions.
HOW AND WHERE YOU WILL WORK
Requires a minimum of 5 days a week onsite; the on-campus requirement is subject to modification based on organizational need.
Occasional evening/weekend work required.
ADDITIONAL SKILLS YOU MAY HAVE
- Experience with other programming languages (Java, R, C++) a plus
- Experience with Azure AI Studio or Foundry a plus.
- Experience in Advancement, fundraising, higher education, or another constituent- or customer-focused environment is helpful but not required.
- Experience with Salesforce Education Cloud, Advancement CRM, Agentforce, Marketing Cloud, Tableau, MuleSoft, or similar advancement technology platforms a plus.
This is an exempt position with the following pay range: $128,294 - $142,549; the role is also eligible for bonuses based on performance and budget.
Babson College offers a comprehensive benefits package for full-time employees working at least 28 hours per week.
- Insurance Coverage: Medical, dental, vision, group life and long-term disability insurance, business travel accident insurance, and mental health benefits.
- Time Off: Starting at 3 weeks of vacation annually, 2 weeks of sick time, 1 week of paid family illness time, 6 weeks of paid parental leave, and 12 paid holidays per year. President’s holidays are determined each year.
- Retirement: Participation in a 403(b) retirement plan with mandatory employee contributions and a 4:1 employer match.
- Additional Benefits: Wellbeing programs, virtual fitness platform, and employee assistance program.
All questions or concerns about this posting should be directed to the Office of Human Resources at [email protected].
Salary Context
This $128K-$142K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Babson College, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($135K) sits 36% below the category median. Disclosed range: $128K to $142K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Babson College AI Hiring
Babson College has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Wellesley, MA, US. Compensation range: $142K - $142K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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