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Head of AI Product
About BetaNXT
BetaNXT is a leading provider of frictionless wealth management infrastructure, real\-time data solutions and enhanced advisor and investor experiences. BetaNXT combines the capabilities of Trading \& Settlement, Asset Movement, Investor Communications and Data and AI Services to provide technology, data and operations services across the investment lifecycle.
Role overview
BetaNXT is seeking a Head of AI Product to join its Product Management leadership team and translate the company’s enterprise AI strategy into differentiated, scalable and commercially successful product capabilities delivered through DataXChange and InsightX. Reporting to the Chief Product Officer, this individual sits as a peer to BetaNXT's current Product Management leadership team, spanning Trading and Settlement, Asset Servicing, Investment Platforms, Operations Platforms, Data Services, Pooled Asset Solutions and Partnerships, co\-owns the integrated product roadmap with them, and works closely with the executive leading AI strategy, technology, sales, finance, legal, risk and marketing.
The role owns product discovery, roadmap development, product requirements, commercial readiness, launch planning, adoption and performance measurement for AI capabilities across the four\-layer platform: the governed data foundation, business\-layer domain packs, the semantic layer and the agentic decision layer. The Head of AI Product will partner with technology leaders to convert product priorities into feasible delivery plans, coordinate with Product Management peers so AI capabilities land inside their existing roadmaps rather than as a parallel track, and establish a repeatable path from innovation and client pilots into governed production products.
Role information
Role information Definition Function
Product Management Level
Senior product leadership, non\-executive Reports to
Chief Product Officer Location
New York, NY or North Carolina, with travel as required Scope
Client\-facing AI capabilities embedded within the current Product Management roadmaps (Trading and Settlement, Asset Servicing, Investment Platforms, Operations Platforms, Data Services, Pooled Asset Solutions and Partnerships), delivered as InsightX and DataXChange Works within Product Management alongside
The current Product Management leadership team, the heads of Trading and Settlement, Asset Servicing, Investment Platforms, Operations Platforms, Data Services, Pooled Asset Solutions and Partnerships, plus the executive leading AI strategy Key responsibilities
- Portfolio and roadmap. Jointly maintain, with the current Product Management leadership team (Trading and Settlement, Asset Servicing, Investment Platforms, Operations Platforms, Data Services, Pooled Asset Solutions and Partnerships), an integrated AI product portfolio and 12 to 18 month roadmap covering DataXChange, InsightX (Compass, Data Studio, Solutions Hub), Val and other approved embedded or stand\-alone AI capabilities, sequenced against each suite's own roadmap rather than planned in isolation.
- Product discovery and definition. Run discovery outside\-in: start from market problems and buyer/user personas, not internal feature requests. Validate market problems through structured voice\-of\-customer interviews and win/loss analysis before writing requirements. Define target users and jobs to be done, establish product charters, translate validated market problems into requirements and acceptance criteria, and make clear prioritization recommendations grounded in market evidence rather than the loudest internal or client voice.
- Market\-driven prioritization. Apply an outside\-in product management discipline (Pragmatic Marketing Framework or equivalent) across the AI portfolio: maintain a current market and competitive landscape view, document distinctive competence for InsightX and DataXChange against competitors, and require a documented market problem and business case before any capability enters the roadmap.
- Delivery partnership. Work with the CTO, engineering leaders, architects and delivery teams to create realistic plans, manage dependencies, make trade\-offs and maintain transparent release commitments.
- Commercialization. Develop packaging and pricing recommendations, positioning and messaging tied to validated market problems, business cases, implementation models, sales tools and buyer\-facing enablement (battlecards, demo scripts, objection handling), launch plans and product\-level commercial metrics in partnership with Finance, Sales and Marketing.
- Adoption and performance. Define and monitor product usage, adoption, client outcomes, service quality and financial performance. Use data and client feedback to refine priorities and improve product value.
- Cross\-suite product coherence. Promote reuse of common data, platform, workflow and governance capabilities across the domain packs (Stock Record, Corporate Actions and others as they come online) and reduce disconnected or duplicative AI point solutions.
- Product Management integration. Sit in the regular Product Management leadership cadence with the heads of Trading and Settlement, Asset Servicing, Investment Platforms, Operations Platforms, Data Services, Pooled Asset Solutions and Partnerships. Represent AI capability status, dependencies and trade\-offs in that forum rather than a separate one, and hold joint accountability with the relevant suite leader for any AI feature shipped inside their roadmap.
- Governance by design. Ensure product requirements include appropriate data rights, security, privacy, model documentation, explainability, Maker\-Checker human oversight, monitoring, versioning, auditability and rollback capabilities, consistent with BetaNXT's model risk management standard.
- Client and market engagement. Lead detailed AI product discussions, discovery sessions and demonstrations. Support major client, sales, board and investor discussions with product\-specific content as requested.
- Product organization. Build and lead a focused AI product team as approved, including product managers or product operations roles, staffed and reviewed within Product Management's existing structure rather than as a separate reporting line. Partner with technology leadership for forward\-deployed engineering and other technical capacity.
- Partner evaluation. Evaluate vendors, integrations and potential partnerships from a product perspective and provide recommendations to the appropriate executive decision\-makers.
Qualifications and experience
- 10 or more years of relevant experience in enterprise product management, financial technology, data products, analytics products or adjacent fields.
- Demonstrated experience taking AI, machine learning, data or workflow products from discovery through production launch and measurable adoption.
- Strong understanding of regulated financial\-services workflows, with wealth management, securities processing, investor communications, tax, fund data or operations experience preferred.
- Experience developing roadmaps, product requirements, business cases, pricing or packaging recommendations and launch plans for enterprise software or data products.
- Trained in and practiced with an outside\-in product management discipline such as the Pragmatic Marketing (Pragmatic Institute) Framework: market problems, buyer personas, win/loss analysis and distinctive competence driving the roadmap, rather than internal or engineering\-led feature requests.
- Ability to work effectively with engineering, architecture, data, security, risk, legal, sales and operations teams in a matrixed environment, including co\-planning roadmaps with peer product leaders who own their own suite's priorities and delivery commitments.
- Sufficient technical fluency to understand AI and data\-platform trade\-offs, model limitations, integration patterns and production\-readiness requirements without acting as the enterprise architect.
- Strong client presence and the ability to explain complex AI and data capabilities to business, product and technical audiences.
- Experience leading product managers or multidisciplinary product teams and developing accountable, outcome\-oriented operating practices.
- Bachelor’s degree or equivalent experience required; advanced degree preferred but not required.
Leadership attributes
- Execution\-oriented and comfortable moving from ambiguity to clear decisions, plans and measurable outcomes.
- Commercially minded, with strong judgment about where AI creates genuine client value versus unnecessary complexity.
- Collaborative, able to operate as a peer inside Product Management, share roadmap ownership with other suite leaders, and influence teams without relying on formal authority.
- Disciplined about scope, risk, evidence and production quality while maintaining urgency and pace.
- Credible with senior clients and internal leaders, but willing to remain close to product detail and delivery.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BetaNXT, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
BetaNXT AI Hiring
BetaNXT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cary, NC, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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