Interested in this AI/ML Engineer role at Blackbaud?
Apply Now →Skills & Technologies
About This Role
About the Role
Blackbaud is expanding how AI agents and copilots support our teams, customers, and mission\-driven organizations around the world. The AI Platform Technical Lead plays a pivotal role in making that a reality. This role leads the team of Value Stream Product Owners (POs), owns the technical depth of Blackbaud's internal AI platform, hands\-on AI fluency, agent architecture evaluation, MCP server governance, and vendor\-neutral technical analysis that informs every platform decision. Reporting to the Director, AI \& Enterprise Platform Enablement, you will serve as the technical force multiplier for the function keeping the platform grounded in what AI can actually do today, what vendors are really delivering, and where enterprise constraints require real architectural judgment.
If you are energized by translating cutting\-edge AI capability into real enterprise workflow change, and by being the person who has a recommendation ready before anyone asks, this role is for you.
What You'll Do:
- Serve as the embedded technical partner and team lead for value stream Product Owners (e.g., Awareness to Revenue, Implementation to Renewal), providing hands\-on guidance on agent design, model selection, and workflow architecture.
- Help value stream POs translate business requirements into well\-scoped agent specifications that the engineering team can build against reliably.
- Bridge the gap between platform capability and value stream delivery ensuring POs know what the platform can support currently and in the future.
- Set the technical direction for how Blackbaud builds and governs AI agents establishing standards, patterns, and guardrails that scale across value streams and teams.
- As the AI Engineer/Builder function grows, serve as the lead architect and day\-to\-day technical lead for a team of two to three engineers focused on API and agentic architecture.
- Represent the technical platform in Steering Committee discussions, PI Planning, and cross\-functional forums providing the analysis that grounds platform decisions in reality.
- Mentor and upskill practitioners across the Champions Network and value stream teams for AI development across the organization.
- Assess new releases, capability claims, and integration options across Claude Enterprise, Copilot/Copilot Studio, Agentforce, and Watson Orchestrate.
- Produce platform recommendations within days, not weeks — with a clear rationale the Director can act on and the ELT can understand.
- Evaluate vendors on technical merit for each use case, with no commercial allegiance to any single platform.
- Own the internal MCP server taxonomy ensuring governance standards apply.
- Coordinate with the CTO's architecture team on crossover zones where internal and external platform tooling intersects.
- Make architecture recommendations on when an internal workflow warrants an MCP server vs. a simpler integration.
- Define design standards for operational agents and review agentic workflow designs before they reach production
- Serve as the technical resource for the internal Champions Network.
- Run technical office hours, review workflow designs, and flag risks before they reach production.
- Prepare technical platform inputs for PI Planning highlighting what capabilities are ready for value stream adoption.
- Partner with the CTO's platform architecture team on technical governance of split\-ownership tools and the boundary between internal tooling and external product capability.
- Track new model releases, agent frameworks, and vendor capabilities and communicate what matters to the Steering Committee.
- Translate field developments into actionable platform implications for the Director and senior stakeholders.
What You Bring:
- 2\+ years experience hands\-on with LLM APIs and agent frameworks, including experience inside or alongside an enterprise AI adoption effort.
- Demonstrated hands\-on AI fluency, experience building prompts, working with more than one LLM API, and experience pushing AI into real workflows preferred.
- Agentic workflow design experience in an enterprise context.
- MCP server architecture literacy with ability to govern a taxonomy and make architecture recommendations.
- Familiarity with multiple Enterprise AI platforms including Claude Enterprise, Microsoft Copilot/Copilot Studio, Salesforce AgentForce, and IBM Watson Orchestrate
- Strong communication skills and able to communicate complex technical platform recommendations to non\-technical and executive audiences
- Experience with AI governance, responsible AI, security, and compliance considerations in an enterprise context.
- Background in consulting or systems integration implementing Claude Enterprise, Copilot, or AgentForce at enterprise scale.
- Passion for making complex organizations better incorporate AI into their teams, workflows, and processes.
Stay up to date on everything Blackbaud, follow us on Linkedin, Twitter, Instagram, Facebook and YouTube
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 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. Senior-level AI roles across all categories have a median of $230,000. 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.