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About This Role
Company Overview
Xen.AI ( https://xen.ai/ )is a pioneering organization specializing in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, and Data Science. With a global presence, we develop innovative, customized SaaS solutions that empower financial institutions like credit unions and community banks to accelerate their digital transformation and enhance operational efficiency.
Overview
We are seeking an energetic and strategic Head or Director of Sales and Business Development to lead our growth initiatives within the AI solutions space tailored for credit unions and community banks. This leadership role offers an exciting opportunity to drive revenue, expand market presence, and foster long\-term client relationships by leveraging cutting\-edge AI technology. The ideal candidate will be passionate about technology sales, possess strong management skills, and thrive in a dynamic B2B environment.
Compensation
For the 1st year all compensation would be 100% revenue based on the client projects that you would help to get and deliver.
After 1st year, based on company's progress we can consider formal corporate positions with market standard salary and equity etc.
Responsibilities
- Develop and execute comprehensive sales strategies to target credit unions and community banks, expanding our footprint in the financial sector.
- Lead the sales and business development efforts for AI solutions for Governance, Risk and Compliance (GRC), targeting credit unions and community banks.
- Lead, motivate, and manage a high\-performing sales team focused on outside sales efforts, ensuring alignment with company goals.
- Cultivate new business opportunities through proactive outreach, networking, industry events, and strategic partnerships.
- Manage key accounts with a focus on account growth, customer satisfaction, and long\-term retention through exceptional customer service.
- Collaborate with marketing teams to develop targeted campaigns that highlight the value of AI\-driven solutions for financial institutions.
- Utilize CRM tools such as HubSpot to track sales activities, forecast revenue, analyze market trends, and optimize sales pipelines.
- Negotiate contracts and proposals with clients, ensuring mutually beneficial agreements that meet both client needs and company objectives.
Qualifications
- Proven experience in outside sales, B2B sales, or technology sales within the financial services or SaaS industry.
- 10\+ years of experience in doing consultative selling and professional services selling with Credit Unions and Community Banks.
- Proven experience in business development within the Credit Unions, Community Banks, Banking, Financial Services, Insurance sectors.
- Experience in selling Governance, Risk and Compliance (GRC) related solutions for community banks and credit unions.
- Proven experience in sales leadership within the financial technology or AI industry.
- Strong management skills with a demonstrated ability to lead teams effectively in a fast\-paced environment.
- Expertise in business development strategies, account management, and negotiation techniques.
- Familiarity with marketing principles and how they integrate with sales initiatives to drive growth.
- Technical proficiency with CRM platforms such as HubSpot; understanding of AI solutions is a plus.
- Excellent analysis skills to interpret market data and identify new opportunities for expansion.
- Exceptional leadership qualities combined with outstanding customer service orientation.
- Authorized to work in United States, No H1B Visa or OPT sponsorship.
Join us to be at the forefront of AI innovation in the financial sector! Bring your energy, strategic mindset, and passion for technology sales to help credit unions and community banks unlock new possibilities through intelligent solutions that transform their operations.
!!!! Please note that we do not work with any recruiters !!!!
Pay: $1\.00 per year
Benefits:
- Flexible schedule
Work Location: Remote
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 Xen.ai, 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. Director-level AI roles across all categories have a median of $247,800.
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.
Xen.ai AI Hiring
Xen.ai has 2 open AI roles right now. They're hiring across AI Product Manager. Based in Remote, US.
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.
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