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About This Role
Basic Function
Lumin Digital is the digital banking platform of choice for credit unions and community banks across the country. The VP of Artificial Intelligence \& Data Engineering will lead the development and execution of Lumin's AI roadmap, encompassing Generative AI, agentic AI, and machine learning, as well as the data capabilities that underpin those technologies.
This leader will build and mentor a high\-performing team of engineers and data scientists, define the technical vision for delivering cutting\-edge AI capabilities, and partner closely with product and engineering leadership to bring transformative, AI\-driven solutions to market. The VP of AI \& Data Engineering reports directly to the Chief Product \& Technology Officer.
Essential Functions, Responsibilities, Experience:
Team Leadership
- Hire, lead, and mentor a team of exceptional software engineers, data scientists, and ML engineers who will build industry\-leading Generative AI, agentic AI, and machine learning capabilities.
- Identify the right mix of talent and skills needed to grow the AI and data engineering function, and create clear development pathways for team members at all levels.
- Foster a culture of innovation, rigor, and continuous learning where the team stays ahead of the rapidly evolving Generative AI and agentic AI landscape.
- Clearly and continuously communicate technical direction, team progress, and delivery details to leadership and stakeholders across the organization.
- Work closely with other development leaders to champion the adoption and integration of Generative AI, agentic AI, and machine learning technologies across all product areas.
AI Strategy \& Innovation
- Define and own the technical strategy for delivering Lumin’s AI roadmap spanning Generative AI, agentic AI, and machine learning, ensuring alignment with overall product and business objectives.
- Evaluate, adopt, and integrate the latest advancements in Generative AI and agentic frameworks to drive product differentiation and create new value for clients.
- Lead the design and implementation of scalable AI capabilities including large language model (LLM) integration, agentic workflows, model development, training infrastructure, and deployment pipelines.
- Continuously assess the AI landscape to identify emerging tools, techniques, platforms, and vendors that can accelerate delivery and sustain competitive advantage.
Data Engineering \& Foundations
- Architect and oversee the build\-out of robust, scalable data pipelines that serve as the foundational infrastructure supporting Generative AI, agentic AI, and machine learning workloads.
- Partner with SRE, Security, and Compliance teams to ensure data pipelines are performant, reliable, and fully compliant with regulatory and security requirements.
- Define and enforce data quality, governance, and observability standards across the data platform to ensure AI models and agents are built on trusted, high\-quality data.
- Drive adoption of best practices for data modeling, storage, and retrieval, including vector databases and retrieval\-augmented generation (RAG) architectures that enable effective AI delivery.
Cross\-Functional Partnership
- Partner closely with Product leadership to understand the roadmap and proactively identify opportunities to leverage Generative AI, agentic AI, and machine learning to deliver breakthrough product capabilities.
- Collaborate with engineering leaders across teams to share AI tooling, best practices, and reusable components that accelerate AI adoption across product areas.
- Engage with clients, prospects, and industry peers to gather insights that inform the AI and data strategy and validate technical direction.
- Translate complex AI and data concepts into clear, compelling narratives for executive, product, and business audiences.
Position Specifications
Education:
- Bachelor’s degree in Computer Science, Data Science, Mathematics, or related field. MS degree or PhD preferred.
Experience:
- At least 10 years of experience in software engineering, data engineering, or machine learning, with at least 3 years in a senior leadership role overseeing AI or data teams.
- Demonstrated hands\-on experience building and deploying production AI systems, ideally Generative AI and agentic AI applications, in B2B SaaS cloud environments.
- Deep expertise in machine learning frameworks (e.g., TensorFlow, PyTorch, scikit\-learn), LLM orchestration tools (e.g., LangChain, LlamaIndex), and data engineering platforms.
- Proven track record of building and growing high\-performing, multi\-disciplinary teams of engineers and data scientists.
- Experience in fast\-paced startup or scale\-up environments where hands\-on technical contribution is expected alongside leadership.
Knowledge, Skills, \& Abilities:
- Strong understanding of AI and data best practices, including model monitoring, versioning, retraining pipelines, RAG architectures, and agentic deployment strategies.
- Experience working in or alongside highly regulated industries (e.g., financial services, healthcare) with a strong understanding of data privacy, compliance, and security requirements.
- Data\-driven approach to decision making, with the ability to derive actionable strategy from complex and ambiguous data.
- Outstanding communication, presentation, and writing skills, with the ability to clearly convey AI and data concepts to non\-technical stakeholders.
- Experience collaborating with product, SRE, and security teams in a fast\-paced, agile development environment.
Travel:
- *Less than 25% \- Up to 65 work days of travel per year for a full time employee*
LIFE AT LUMIN DIGITAL
Lumin Digital is a trailblazer in digital banking solutions, driven by a unique approach to technology, service, and people. We empower credit unions and banks by creating cutting\-edge digital experiences that continuously serve, engage, and grow their membership base — and as a 100% cloud\-native company, we're purpose\-built to unlock the full advantages of the cloud for financial institutions and their users.
At Lumin, we thrive on curiosity and innovation. Our culture is built on trust in our expertise and decisions, respect for diverse perspectives and talents, and boldness in pursuing new ideas. These values shape a workplace where collaboration thrives, ideas flourish, and new possibilities are discovered every day. We encourage our team to explore, experiment, and challenge the status quo — because continuous improvement isn't just a goal, it's how we operate.
Benefits include: We take care of our people with medical, dental, and vision insurance, a 401(k) with company match, flexible PTO plus 12 paid holidays, paid sick leave, and paid parental and family leave. We also offer a lifestyle spending account, tuition reimbursement, and a cell phone stipend. Additional details are provided during the interview process.
Lumin Digital is an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender identity, or any other legally protected basis. For more information, visit lumindigital.com.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $300K-$320K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $189K across 161 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Lumin Digital, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $213,800 based on 583 positions with disclosed compensation. This role's midpoint ($310K) sits 45% above the category median. Disclosed range: $300K to $320K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Lumin Digital AI Hiring
Lumin Digital has 1 open AI role right now. They're hiring across AI Product Manager. Based in Remote, US. Compensation range: $320K - $320K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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