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About the Role
The I2R value stream owns one of Blackbaud’s most consequential customer relationships—from implementation handoff through renewal. The Product Owner, AI Workflows – I2R is responsible for redesigning how that journey gets delivered, using AI to reduce friction, surface risk earlier, and accelerate the path to customer value. This role owns delivery of the AI workflow roadmap for the I2R value stream. You will design, prioritize, and deliver the AI\-assisted workflows that change how implementation, onboarding, and renewal actually operate, working across business domain leads and the AI Platform Technical Lead, turning AI possibilities into‑real workflow change, every day.
What You’ll Do
- Own the AI workflow roadmap for I2R, defining which workflows to redesign across implementation, onboarding, support, and renewal, aligned to enterprise AI strategy and I2R business priorities.
- Identify high‑value use cases within your value stream where agents can meaningfully change how work gets done
- Own the backlog of AI workflow initiatives for I2R, prioritizing by customer impact and workflow readiness across the implementation\-to\-renewal lifecycle and drive delivery through to production.
- Define epics, features, and acceptance criteria that enable reusable, secure, and scalable AI agent patterns.
- Balance innovation with reliability, privacy, and responsible AI requirements.
- Ensure platform capabilities are designed once and leveraged many times across teams.
- Act as the connective tissue between engineering, data science, UX, security, legal/compliance, and product teams.
- Drive alignment on roles and responsibilities for AI agent development, deployment, and lifecycle management.
- Partner with platform and application teams to onboard them to AI agent tooling and best practices.
- Represent the AI platform in planning forums, reviews, and executive updates.
- Ensure AI agents align with Blackbaud’s standards for security, privacy, compliance, and ethical AI.
- Apply the guardrails, operating models, and workflow standards defined by the Agentic Process Lead and flag where they need refinement based on domain reality.
- Collaborate with risk and compliance partners to evolve governance without slowing innovation.
- Champion adoption of AI platform capabilities across Blackbaud.
- Define success metrics for AI agent usage, quality, and business impact.
- Identify friction points and continuously improve the developer and user experience.
- Help teams move from pilots to production with confidence.
What You Bring:
- 8\+ years experience designing and delivering workflows, internal tooling, or shared services in an enterprise environment.
- Experience building or scaling AI platforms, AI\-assisted or agentic technologies strongly preferred.
- Proven ability to map a current\-state workflow, identify friction points, and redesign it for AI augmentation with the business and engineering teams
- Strong understanding of agile product ownership and backlog management.
- Proven ability to influence and align cross\-functional teams without formal authority.
- Excellent communication skills with demonstrated ability to translate complex technical concepts into clear product decisions.
- Familiarity with AI governance, responsible AI, security, and compliance considerations.
- Experience working in a regulated or enterprise environment.
- Passion for mission\-driven technology and platform thinking.
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Blackbaud powers social impact through purpose‑driven technology and responsible AI. Guided by our *Intelligence for Good®* vision, we’re building a culture where innovation, trust, and human expertise come together to help organizations make a greater difference in the world.
Blackbaud is proud to be an equal opportunity employer and is committed to maintaining a diverse and inclusive work environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, physical or mental disability, age, or veteran status or any other basis protected by federal, state, or local law.
The starting base pay is $117,200\.00 to $157,500\.00\. Blackbaud may pay more or less based on employee qualifications, market value, Company finances, and other operational considerations.
Benefits Include:
- Medical, dental, and vision insurance
- Remote\-flexible workforce
- Wellness Programs
- 401(k) program with employer match
- Flexible paid time off
- Generous Parental Leave
- Donations for Doers
- Pet insurance, legal and identity protection
- Tuition reimbursement program
Salary Context
This $117K-$157K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Blackbaud, 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($137K) sits 37% below the category median. Disclosed range: $117K to $157K.
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.
Blackbaud AI Hiring
Blackbaud has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $157K - $157K.
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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