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About This Role
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Kapitus is seeking a Senior Vice President, Data & AI Strategy and Transformation to serve as the Chief Data and AI Officer's principal leader for enterprise Data & AI architecture, transformation execution and technical delivery. Reporting to the CDAO, the SVP will translate and execute Kapitus' approved enterprise Data and AI vision to sequenced target state architecture, operating mechanisms, investment roadmap, reference patterns, and measurable transformation outcomes.
The SVP will lead the practical conversion of fragmented data, workflows and point solutions into trusted enterprise data, reusable intelligence, governed automation, and measurable business outcomes. This is a player-coach role for a leader who move fluently between executive decision making and technical implementation. Will be advising the CDAO on material choices while remaining directly engaged in architecture, prototyping, design assurance, and delivery.
What you will do:
Strategy and Operating Model
- Lead the development and execution of the enterprise Data and AI strategy for CDAO and Executive team approval by translating corporate priorities in sales, credit, risk, finance, and operations—into a sequenced transformation plan with clear outcomes, milestones, investments, and executive decision points.
- Design, operationalize and continuously improve the Data and AI operating model including decision rights, product ownership, domain accountability, architecture governance, responsible-AI controls, funding, and value realization — and advise the CDAO and executive leadership on trade-offs, sequencing, vendor choices, and platform economics.
Enterprise Data Architecture and Data Products
- Design and implement the enterprise data architecture — data-fabric capabilities (metadata-driven integration, active lineage, policy enforcement, discoverability) with data-mesh principles (domain ownership, product thinking, federated governance) on a layered Lakehouse/medallion foundation — with reference architectures, standards, data contracts, and guardrails spanning integration, APIs, orchestration, metadata, data quality, master data, semantic and knowledge models, ML/AI, security, observability, and FinOps.
- Enable VP of Data Products to prioritize and lead data products from concept through production adoption — named owners, defined consumers, source contracts, quality thresholds, lineage, service levels, and lifecycle controls — serving certified BI, APIs, events, files, machine-learning features, and AI context.
- Provide hands-on leadership in data modeling, SQL and transformation design, orchestration, testing, observability, and production-readiness reviews, replacing duplicate pipelines, reports, and point solutions with governed, reusable products.
Agentic AI Platform and Orchestration
- Define the strategy and target architecture for governed agentic AI: platform control plane, runtime orchestration, model and provider strategy, tool integration, context engineering, memory governance, evaluation, and observability.
- Architect and build a reusable orchestration harness that binds workflows to approved skills, bounded agents, registered tools, output contracts, approval gates, stop conditions, and auditable outcomes — permitting agent autonomy only within explicitly defined scope and policy, with governed registries and reusable libraries for workflows, skills, agents, tools, context, memory, and models that separate immutable evidence from curated knowledge.
- Implement evaluation packs, baseline-versus-candidate testing, promotion gates, rollback paths, and post-deployment monitoring — Implement and enforce release controls under the AI governance policy, including evidence, approval, and rollback readiness.
- Establish governed AI-assisted engineering across architecture, code generation, documentation, testing, and review, with multi-stage review patterns ensuring no single person, model, or automation closes its own loop.
Governance and Responsible AI
- Operationalize data ownership, stewardship, metadata, lineage, quality, certification, classification, privacy, and retention as embedded platform and product controls.
- Create responsible-AI and model-governance practices — use-case intake, model risk, human oversight, explainability, fairness, monitoring, and change control — with evidence-ready traceability from business intent through data, model, agent, and outcome, so the enterprise can safely operate AI in regulated, customer-facing, credit, underwriting, and reporting environments.
Leadership, Organization and Vendor Accountability
- Lead the Data and AI Strategy, Enterprise Architecture and Transformation function working in partnership with heads of Governance, BI, Data Products, ML and Data Platform & Engineering — with a culture of technical excellence, reuse, evidence, and accountability; operate architecture review and delivery meetings, support CDAO in executive steering and AI governance forums by enabling translation of technical choices into clear business implications.
- Own vendor strategy, statements of work, acceptance criteria, and knowledge transfer — retaining internal ownership of architecture, product definitions, and platform truth — and manage transformation budget and vendor spend, tying continued investment to evidence, adoption, and measurable outcomes for CDAO and executive approval.
- Be able to enter a working session, inspect the evidence, challenge the design, build a prototype or reference pattern, troubleshoot delivery constraints, and guide the team to a production-ready outcome.
- Personally develop or review architecture diagrams, product specifications, data contracts, model and agent interaction patterns, evaluation designs, and release criteria — working directly with SQL, data models, transformation frameworks, orchestration, cloud data platforms, AI model APIs, prompts, and agent workflows when critical decisions require first-hand validation, and building reference implementations before scaling them through internal teams and vendors.
- Lead difficult design and delivery decisions from evidence rather than presentations, vendor assertions, or high-level status reporting.
- Inspire and create a motivating environment through coaching, mentoring, and enablement
- Provide opportunities to participate in both formal and informal developmental activities
- Working with senior leaders and department head, set expectations for all team members and hold employees accountable for results
- Lead hiring, performance management, compensation, and evaluation of team members
- Collaborate with Kapitus leadership and HR to continually refine and modify career growth and succession plans to meet changing business needs
What we're looking for:
- 15+ years of progressive experience across enterprise data, analytics, artificial intelligence, architecture, engineering, and technology transformation, with significant leadership responsibility in complex organizations.
- Bachelor's degree in a technical, quantitative, or business field; equivalent executive and technical experience will be considered.
- Demonstrated success conceiving and executing enterprise Data & AI strategies, target architectures, operating models, and multi-year transformation roadmaps that produced measurable business outcomes.
- Deep hands-on experience designing and implementing data-fabric and data-mesh architectures, lakehouse/medallion platforms, semantic layers, metadata, lineage, data quality, and master data management — and personally implementing reusable data products across BI, APIs, events, files, machine-learning features, and AI consumption patterns.
- Demonstrated hands-on experience architecting and building a governed enterprise agentic AI platform or orchestration harness — workflow orchestration, reusable skills, bounded agents, tool integration, context engineering, memory governance, release gates, and rollback — beyond deploying packaged copilots, including evaluation frameworks that compare baseline and candidate behavior, detect regressions, and retain rollback evidence.
- Strong hands-on technical fluency with SQL, Python or comparable languages, data modeling, transformation frameworks such as dbt, orchestration platforms such as Dagster or Airflow, cloud data platforms such as Snowflake, and modern CI/CD, testing, security, and observability practices.
- Experience in financial services, lending, credit, payments, risk, or another regulated, data-intensive industry.
- Track record of building multidisciplinary teams, influencing C-suite stakeholders, leading major vendors, and managing budgets — with the ability to explain complex architecture and AI topics in plain business language.
Preferred:
- Graduate degree in a technical, quantitative, or business discipline.
- Experience creating original enterprise architecture, data-product, governance, or agentic-AI frameworks — including ontology and knowledge graphs, context and memory architectures, model and provider portability, prompt/context caching, and cost-optimized AI runtime design — and converting them into reusable, tested methods.
- Experience leading a mixed internal, offshore, and vendor delivery model while retaining internal architecture, product, governance, and acceptance authority.
Kapitus Total Rewards Package Includes:
- Competitive Base Salary Range of $187,200-$300,300 Kapitus is providing this as a good faith salary range to comply with applicable law. The applicant's final salary will depend on a number of factors including the applicant's geographic location, skills, and experience.
- Annual Incentive Compensation Eligibility –Up to 20% annually
- Health Insurance: Comprehensive medical, dental, and employer-paid vision plans through UnitedHealthcare (UHC), with various coverage levels available to meet the needs of our employees and their families. Additional perks through UHC include: Sweat Equity, free subscription to the Calm App, UHC rewards, Real Appeal, and Quit For Life.
- Flexible Spending Account: Set aside pre-tax dollars from your paycheck to pay for qualified out-of-pocket medical, dental, vision, pharmacy or dependent care expenses.
- Lifestyle Spending Account: Employer sponsored post-tax benefits that allow reimbursement for expenses related to physical, mental and financial well-being.
- 100% Company Paid Insurances: Kapitus fully covers the cost of basic short-term and long-term disability insurance, as well as vision insurance, ensuring our employees have comprehensive protection without any personal expense.
- Voluntary Insurance: Supplemental life insurance as well as enhanced short- and long-term disability coverage are available through Mutual of Omaha, providing additional security for our employees. Additionally, Colonial Accident and Hospitalization insurances are also available, offering further protection against unforeseen events.
- Paid Maternity and Parental Leave: Beyond state-mandated leave policies, Kapitus provides company-paid maternity and parental leave, supporting our employees during important family milestones.
- Commuter Benefits: We offer pre-tax benefits on parking and commuter expenses to cover travel to and from work.
- LifeBalance Program: Enhance your lifestyle with our LifeBalance membership, which offers discounts on outdoor activities, the arts, health, and fitness. Additional benefits include: + Pet and car insurance discounts. + Financial services such as LegalShield. + Relaxation and stress management tools.
- Plum Benefits Discount Program: Access exclusive discounts on shows, travel, car rentals, and more, enriching your personal and family life.
- Tuition Reimbursement: Pursue further education with up to $5,000 annually in tuition reimbursement, plus opportunities to attend relevant conferences and career development events. Managed through our LSA plan, Kapitus Academy.
- Travel Reimbursement: We also offer travel reimbursement for all work-related travel, supporting your involvement in career and personal development activities.
- Paid Time Off and Sick Time.
- Retirement Benefits: Our 401K plan is managed through Fidelity. To support your long-term financial goals, the company provides a 25% match on your contributions, up to 6% of your annual salary.
About Kapitus:
Kapitus is one of the most reliable and respected names in small business financing. As both a direct lender and a marketplace built with a trusted network of lending partners, we can provide small businesses with the financing they need when, and how it is needed. We have spent our entire existence building a culture that makes us excited to come to work in the morning. Our company is fast paced, teammates need to be self-directed and have an internal motivation to do the right thing, even when the right thing takes a lot of hard work. We show our teammates our appreciation by offering great benefits, competitive pay and solid opportunity for growth.
Company Mission: At Kapitus, our mission is to help small business owners grow their organizations by providing tailored, transparent, and ethical financing solutions. We invest in every business owner's story and we are dedicated to building lasting relationships to champion their goals. We promise to keep the best interests of our clients at the center of the financing process by operating with transparency, fairness, and integrity.
Consideration will be given to qualified remote candidates residing in states where Kapitus and/or one of its subsidiaries has an established physical presence.
Salary Context
This $187K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Kapitus, 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 $211,950 based on 13,048 positions with disclosed compensation. This role's midpoint ($243K) sits 15% above the category median. Disclosed range: $187K to $300K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Kapitus AI Hiring
Kapitus has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Based in Arlington, VA, US. Compensation range: $189K - $300K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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