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About This Role
Risepoint is an education technology company that provides world\-class support and trusted expertise to more than 100 universities and colleges. We primarily work with regional universities, helping them develop and grow their high\-ROI, workforce\-focused online degree programs in critical areas such as nursing, teaching, business, and public service. Risepoint is dedicated to increasing access to affordable education so that more students, especially working adults, can improve their careers and meet employer and community needs.
### Role Overview
Risepoint is poised to transform the organization using technology and an AI\-first mindset. We are seeking a visionary and execution\-focused VP, AI Transformation and Enterprise Architecture , to lead the next phase of our AI\-powered transformation. This executive will be responsible for working with cross functional teams for building and scaling AI\-driven products, driving enterprise\-wide AI adoption, shaping technology strategy and architecture, and leading complex technology integration initiatives, including post\-merger and acquisition (M\&A) integrations.
This role requires a unique blend of deep engineering leadership, enterprise architecture expertise, AI innovation, and strategic consulting capabilities. The ideal candidate is equally comfortable defining long\-term technology strategy, building high\-performing engineering organizations, and rolling up their sleeves to solve ambiguous, high\-impact business challenges.
The VP will partner closely with executive leadership, product, engineering, operations, and business teams to accelerate AI adoption, modernize technology platforms, and establish a scalable architecture foundation for future growth.
### Key Responsibilities
### Technology AI Transformation Leadership
- Lead and scale AI adoption across all technology teams at Risepoint.
- Establish engineering best practices, operating models, and delivery frameworks for AI and machine learning initiatives.
- Oversee the design, development, deployment, and lifecycle management of Agentic AI, Generative AI, and intelligent automation solutions.
- Drive technical excellence in AI application development, model integration, orchestration frameworks, evaluation, observability, and governance.
### Enterprise AI Transformation \& Adoption
- Define and execute the enterprise\-wide AI strategy, driving the adoption and integration of AI across technology, operations, and business functions.
- Partner with executive leadership and cross\-functional stakeholders to identify, prioritize, and scale high\-impact AI initiatives that improve operational efficiency, customer experience, employee productivity, and business outcomes.
- Lead the development and implementation of AI governance, architecture standards, risk management frameworks, and responsible AI practices to ensure scalable and sustainable adoption.
- Collaborate across Product, Operations, Marketing, Finance, HR, and other business units to embed AI capabilities into core business processes, workflows, and customer\-facing solutions.
- Serve as a strategic advisor and thought leader on emerging AI technologies, translating complex technical concepts into clear business value and executive\-level recommendations.
- Champion organizational change management, workforce enablement, and AI literacy initiatives to accelerate enterprise\-wide adoption and maximize value realization.
### Enterprise Architecture
- Own and evolve the company's enterprise architecture strategy, ensuring alignment with business objectives and growth plans.
- Establish architecture principles, standards, and governance processes across applications, data, integrations, infrastructure, and AI platforms.
- Create scalable technology roadmaps that support innovation while maintaining operational excellence and security.
- Ensure interoperability, scalability, reliability, and maintainability across the enterprise technology landscape.
### Drive M\&A Integrations
- Lead technology due diligence and integration planning for mergers, acquisitions, and strategic partnerships.
- Develop and execute post\-M\&A technology integration strategies, including systems consolidation, data migration, application rationalization, and architecture alignment.
- Partner with business leaders to accelerate value realization from acquisitions while minimizing operational disruption.
- Establish repeatable integration frameworks and playbooks to support future M\&A activity.
### Qualifications
- 15\+ years of progressive technology leadership experience, including leading large\-scale engineering organizations.
- Proven experience building and scaling software engineering teams and technology organizations.
- Demonstrated success delivering AI\-powered products and platforms in production environments.
- Experience leading enterprise\-wide technology transformation initiatives.
- Deep expertise in enterprise architecture, technology strategy, and systems integration.
- Significant experience managing complex cross\-functional programs involving both technical and business stakeholders.
- Experience leading technology integration efforts associated with mergers, acquisitions, or large\-scale organizational transformations.
- Passion for AI\-first strategies and leveraging emerging technologies to accelerate business outcomes.
- Experience in fast\-moving industries and organizations that have technology systems supporting multiple brands within the same ecosystem.
Preferred Experience
- Background in top\-tier management consulting (e.g., McKinsey, Bain, BCG, Accenture, Deloitte, EY\-Parthenon, or similar).
- Experience in the Higher Education industry
- Hands\-on experience with Agentic AI architectures, autonomous systems, multi\-agent orchestration frameworks, and Generative AI platforms.
- Experience deploying and scaling LLM\-based applications and AI\-driven automation solutions.
- Expertise across modern cloud platforms, data platforms, APIs, integration architectures, and enterprise SaaS ecosystems.
- Experience operating within high\-growth, rapidly evolving organizations.
Technical Expertise
- Agentic AI and Generative AI architectures and implementation
- Large Language Models (LLMs), RAG, vector databases, and AI orchestration frameworks
- AI platform engineering, MLOps, LLMOps, and model lifecycle management
- Enterprise architecture and technology governance
- Cloud\-native application architectures and distributed systems
- Data platforms, analytics, and integration architectures
- API strategies, event\-driven architectures, and middleware platforms
- Cybersecurity, compliance, and responsible AI practices
Leadership Competencies
The successful candidate will demonstrate:
- Strategic Vision: Ability to define long\-term technology and AI strategy aligned with business objectives.
- Executive Influence: Strong executive presence with the ability to influence senior stakeholders across the organization.
- Builder Mindset: Proven ability to create structure in ambiguous environments and build organizations, processes, and platforms from the ground up.
- Transformation Leadership: Experience leading large\-scale organizational and technology change initiatives.
- Consultative Problem Solving: Ability to navigate complex business challenges and translate strategy into execution.
- Operational Excellence: Strong focus on execution, accountability, and measurable outcomes.
Entrepreneurial Agility: Thrives in fast\-paced environments with evolving priorities and significant white\-space opportunities.
*Risepoint is an equal\-opportunity employer and supports a diverse and inclusive workforce.*
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Risepoint, 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 $218,750 based on 3,817 positions with disclosed compensation.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Risepoint AI Hiring
Risepoint has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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