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About This Role
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Kapitus is seeking a high-impact, execution-oriented leader for the Principal Data & AI Governance role, serving as the enterprise governance architect and primary practitioner for its Enterprise Data Governance program. Reporting to the VP, Data Governance & BI within the CDAO organization, this senior individual-contributor role combines enterprise framework design, functional leadership, and direct operationalization.
The role will architect and operationalize Kapitus's enterprise Data Governance framework while establishing the initial risk-based Model & AI Governance capability. The successful candidate will translate governance policy and standards into adoptable operating practices and increasingly automated controls across data, analytics, reporting, machine learning, generative AI, AI-enabled workflows, and applicable third-party AI services.
This is a Player-Architect role for a leader who balances strategic framework design with direct operational delivery. The role defines governance requirements, control objectives, evidence expectations, and acceptance criteria; engineering and platform teams implement and operate associated technical controls. The Principal partners closely with those teams to ensure controls are practical, appropriately designed, and demonstrably operating as intended.
What You Will Do
- Architect and operationalize Kapitus's enterprise Data Governance framework, including policies, standards, decision rights, escalation and exception processes, evidence requirements, governance reporting, and operating cadences.
- Design and activate a federated governance model that enables Business Data Owners, Business Data Stewards, and Technical Data Custodians to apply enterprise standards within their domains.
- Build durable internal governance capability by codifying standards, operating practices, stewardship guidance, workflows, and decision records that reduce dependency on external consulting support over time.
- Establish governance practices for critical data assets, including business glossary, Critical Data Elements, metadata, lineage, data contracts, data classification, retention, lifecycle, and certification.
- Define the enterprise Data Quality framework, including quality dimensions, business rules, monitoring expectations, issue and remediation practices, risk reporting, and risk-based release or promotion criteria.
- Partner with Data Engineering, Analytics Engineering, BI, Product, and Enterprise Architecture to embed governance requirements in data products, dashboards, reports, semantic models, and analytical delivery processes.
- Serve as the functional product owner for Atlan and related governance capabilities, defining business priorities, workflows, adoption requirements, and evidence needs in partnership with Governance Platform Engineering.
- Establish and operationalize a risk-based Model & AI Governance framework covering analytical models, machine learning, generative AI, AI-enabled workflows, agentic AI use cases, and applicable third-party AI services.
- Define proportionate model and AI lifecycle requirements for intake and risk tiering, inventory and ownership, documentation, review and approval, controlled implementation, monitoring, material change, issues and exceptions, and retirement. Define governance expectations for model and AI evaluation evidence; performance, drift, and outcome monitoring where applicable; approved-data use; grounding and retrieval for generative-AI use cases; logging, access controls, traceability, human oversight, and incident response.
- Establish risk-based expectations for agentic AI, including autonomy levels, tool and action permissions, human-in-the-loop or human-on-the-loop oversight, evaluation evidence, and audit trails.
- Partner with Risk, Compliance, Legal, Information Security, Procurement, Product, Operations, and technical teams to align governance requirements with enterprise risk and third-party risk practices. This role does not replace independent model validation where such validation is required.
What You Bring
- 10+ years of experience in Data Governance, Data Management, Data Quality, or related data disciplines, including demonstrated success building or materially maturing enterprise governance capabilities.
- Experience developing federated stewardship programs, governance operating models, policies, standards, workflows, decision rights, and governance forums.
- Strong practical knowledge of business glossary design, metadata management, data cataloging, data lineage, Critical Data Elements, Data Quality, data classification, retention, lifecycle management, and certification; Atlan experience is preferred.
- Working knowledge of model and AI lifecycle governance, including inventory, risk tiering, ownership, documentation, evaluation, monitoring, change management, and evidence expectations.
- Direct experience in commercial or small-business lending, banking, fintech, payments, insurance, or another similarly regulated financial-services environment is strongly preferred. Ability to engage credibly with business, risk, and control stakeholders on financial-services data, reporting, and governance requirements.
- Modern data-platform fluency, including Snowflake, dbt, Tableau, Power BI, Sigma, semantic layers, metadata/catalog platforms, data-quality monitoring, and automated lineage.
- Working knowledge of machine-learning lifecycle concepts and modern AI patterns, including generative AI, retrieval-augmented generation, evaluation, monitoring, AI-enabled workflows, and proportionate human-oversight practices.
- Exceptional communication and influence skills, with the ability to translate governance requirements into practical, repeatable processes and implementable controls for executives, business stakeholders, engineers, risk partners, and auditors.
- A bachelor's degree in a relevant field, or equivalent relevant experience, is preferred; a master's degree is a plus.
- Relevant certifications such as CDMP or IAPP AIGP, and NIST AI RMF or ISO/IEC 42001 training, are valued but are not required or substitutes for demonstrated implementation experience.
Kapitus Total Rewards Package Includes:
- Competitive Base Salary Range of $117,800 to $189,000. 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 10% 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 $117K-$189K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($153K) sits 28% below the category median. Disclosed range: $117K to $189K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Kapitus AI Hiring
Kapitus has 2 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer. Based in Arlington, VA, US. Compensation range: $189K - $252K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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