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About This Role
Description
Job Description
POSITION: AI Integration & Solutions Engineer
REPORTS TO: Director of Technology Strategy & Innovation
POSITION TYPE: 12-month
START DATE: August 2026
COMPENSATION: $80,942 - $99,154 - Home Office Grade 8
At YES Prep, work for a mission that impacts the life trajectory of students and their families. YES Prep creates pathways to extraordinary opportunity through best-in-class development, competitive compensation and benefits, and recognition for your impact. Grow as an authentic leader and be part of a high-performing, team-oriented culture.
Some quick stats about us:
- 21,000+ Students
- 2,400 Team Members
- 16 Secondary Campuses
- 11 Elementary Campuses
- 92% Students Who Qualify for Free/Reduced-Price Lunch
- 96% Hispanic & African-American Students
- 90% Students Who Are Accepted to a 4-Year University
- 88% First-Generation College Students
- Ranked in the Top 100 High Schools in the Nation, U.S. News & World Report
We serve the students of Houston, Texas, the 4th largest city across the United States. There are over 20 public-school districts and other great public charter school options within Houston’s city limits serving our increasing population.
THE OPPORTUNITY
YES Prep needs a dynamic AI Integration & Solutions Engineer with a robust set of experiences, to make our mission a reality. As a YES Prep employee, you will achieve jaw-dropping results, create pathways to opportunity for our students, and build transformative relationships.
To put it simply, you will design, deploy, and support AI-enabled solutions that solve real organizational needs and strengthen district operations. You will guide AI integration from evaluation through rollout, documentation, and continuous improvement, helping YES Prep adopt AI in ways that are effective, responsible, and sustainable.
THE ROLE
When you join the YES Prep team, some of the work and responsibilities will include:
- Leading applied AI integration across instructional and operational workflows, ensuring AI capabilities are embedded into the district’s digital ecosystem;
- Designing, building, and deploying AI-enabled solutions and agents using approved platforms;
- Maintaining a district portfolio of AI solutions, including reference solutions, reusable patterns, and guardrail-aligned templates;
- Vetting AI tools, features, and embedded capabilities through the software intake and portfolio review process;
- Evaluating AI capabilities within existing platforms to assess value, overlap, readiness, and required guardrails;
- Advising on buy vs. build decisions, including when agent development can meet needs versus when new software is justified;
- Embedding responsible AI practices by designing guardrails into solutions and modeling effective human-AI collaboration;
- Supporting pilots, phased rollouts, and post-deployment feedback loops to validate impact and drive continuous improvement;
- Enabling organizational readiness through training, playbooks, example use cases, and working sessions for educators and staff;
- Monitoring how AI tools connect across YES Prep’s technology ecosystem and surfacing risks related to fragmentation, overlap, or unmanaged spend;
- Serving as an escalation point for emergent AI issues related to deployed solutions;
- Maintaining clear documentation of solution designs, integration dependencies, data flows, and decision rationale for technical and non-technical stakeholders;
- Collaborating with the Enterprise Systems Engineer and broader technology team on integration patterns and data readiness for AI initiatives.
- Additional duties as assigned.
THE PERSON
We seek staff with a multitude of backgrounds, talents, interests, and ideas. Certain traits consistently shine through our most successful employees: passion for the mission, drive for results, care for students, and resilience. Other traits needed and desired for this role are:
THE MUST HAVES
- Bachelor’s degree from an accredited four-year institution
- 2 years of hands-on experience building AI agents, LLM-based solutions or workflow automations in an organizational setting
- Experience evaluating AI tools and embedded platform features, including vendor data practices and integration fit
- Ability to integrate AI tools with enterprise platforms such as M365, Google Workspace, or SIS/LMS systems
- Experience translating business or instructional needs into AI-enabled workflows and practical solutions
- Strong communication skills, with the ability to translate technical decisions for non-technical stakeholders
- Ability to take initiative, plan projects, prepare materials, document work, and lead from ambiguity to completion
- Openness to giving and receiving frequent feedback
- Strong judgment related to responsible AI, data privacy, security, and change management
THE MUST HAVE BELIEFS
- Belief in and commitment to the mission and core values of YES Prep Public Schools
- Desire to disrupt systemic status quos in education
- Willingness to go above and beyond to disrupt said status quos
- Passion to develop resiliency through a relentless pursuit of jaw-dropping results
- Conviction that high expectations + high accountability = high performance and high growth
THE NICE TO HAVES
- Post-graduate degree in educational technology, information systems, business, public administration, or a related field
- Experience in K-12 education or a mission-driven nonprofit, ideally in innovation, systems strategy, or digital transformation
- Familiarity with low-code/no-code platforms such as Power Automate, Make, or n8n
- Background in enterprise systems, integration design, or solution design
- Experience developing AI solutions using Microsoft Copilot Studio
- Relevant AI or automation certification
THE PERKS
- Compensation – This position is an exempt, salaried position; offers commensurate with experience.
- Health and Supplemental Insurance – A $0 monthly medical option or a $24 monthly option that includes $50 YES Prep Paid Health Savings Account contribution, in addition to a variety of supplemental benefits including Dental, Vision, Disability, Life, etc. Employees must work 20+ hours a week to be eligible for a full spectrum of benefits, and 10 hours a week minimum for medical benefits.
- Retirement Plans – Participation in Teacher Retirement System of Texas and an optional 457b Retirement Plan.
- Paid Time Off – Employees working 20+ hours a week receive 15 days of Paid Time Off in addition to the system closures during the Academic Calendar.
- Paid Leave Programming – In the event you need to be out due to medical needs (including maternity and paternity leave), YES Prep offers paid leave, paid out at 100%, based on eligibility and tenure.
- Professional Development - YES Prep values longevity and performance over time. We invest in developing leaders through high-quality programs, effective coaching, and ongoing professional development.
- Award Winning Culture – The strong cultures of our campuses and Home Office are just one of many reasons we were named Houston’s Best Place To Work in 2018, 2019, 2021 and 2025. We won the 2012 Broad Prize for Excellence in Urban Education and were a 2016 finalist as well.
YES Prep is an equal opportunity employer and does not discriminate against any employee or applicant on the basis of race, color, ethnicity, religion, gender, sexual orientation, gender expression, national origin, disability, age, marital status, military status, pregnancy, or parenthood.
Salary Context
This $80K-$99K 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 YES Prep Public Schools, 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 ($90K) sits 58% below the category median. Disclosed range: $80K to $99K.
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.
YES Prep Public Schools AI Hiring
YES Prep Public Schools has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US. Compensation range: $99K - $99K.
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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