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About This Role
Description:
Asurint has redefined screening by blending advanced technology with human insights, creating a smarter, more effective way to screen. Our compliant, data\-driven, tech\-enabled solutions deliver faster, more reliable results to empower confident decisions. At Asurint, each member of our team plays and integral part as we work together to drive informed decisions through advanced screening solutions for fair and safe workplaces and communities.
Summary
The AI Product Strategy \& Transformation Director is responsible for establishing, leading, and scaling Asurint’s enterprise artificial intelligence capability. This role serves as the primary AI strategist and transformation catalyst by identifying, prioritizing, implementing, and governing AI\-enabled business solutions across all functions; and partners closely with business and technology stakeholders to identify high\-value opportunities, redesign workflows, deploy practical AI solutions, inspire adoption, and drive measurable business outcomes.
Responsibilities
- Develops and maintains Asurint’s enterprise AI strategy, roadmap, operating model, governance framework, and implementation priorities in alignment with corporate objectives and business needs.
- Partners closely with leaders across Operations, Product, Technology, Compliance, Legal, HR, Finance, Client Success, and Executive Leadership to identify business challenges and translate them into practical, AI\-enabled solutions.
- Builds and manages an AI opportunity pipeline by identifying, evaluating, and prioritizing high\-value use cases across the organization.
- Leads enterprise AI initiatives from concept through deployment, including opportunity assessment, requirements gathering, business case development, pilot execution, implementation planning, change management, adoption, and optimization.
- Serves as the organization’s AI subject matter expert, maintaining deep knowledge of generative AI, large language models, workflow automation, AI agents, intelligent search, knowledge management, adoption, and optimization.
- Designs, prototypes, configures, and deploys AI\-enabled solutions including copilots, workflow automations, knowledge assistants, decision\-support tools, document intelligence solutions, and other business productivity applications.
- Evaluates build\-versus\-buy options and recommends AI platforms, technologies, and solutions based on business value, scalability, security requirements, integration capabilities, implementation complexity, and return on investment.
- Establishes a structured intake, evaluation, prioritization, and governance process for AI\-related requests and opportunities across the enterprise.
- Drives adoption and business utilization of AI solutions through training programs, user enablement, communication strategies, best practices, and organizational change management initiatives.
- Partners with Technology, Security, Compliance, Legal, and Data Governance stakeholders to ensure AI solutions meet organizational and AI governance standards related to responsible AI usage, data privacy, human oversight, risk management, monitoring, approval workflows, and compliance requirements as well as applicable regulatory obligations for sensitive and regulated data.
- Develops success metrics, operational KPIs, and value realization frameworks to measure productivity improvements, operational efficiencies, cost savings, quality enhancements, risk reduction, and business impact.
- Monitors AI initiative performance, user adoption, business outcomes, and return on investment; continuously refines solutions and priorities based on measurable results.
- Facilitates discovery sessions, operational reviews, workshops, and working sessions to identify workflow inefficiencies, automation opportunities, and AI use cases.
- Builds and maintains a portfolio and pipeline of AI opportunities across the organization, balancing quick wins with strategic transformation initiatives.
- Creates reusable AI frameworks, standards, templates, implementation methodologies, and best practices to accelerate enterprise adoption and scalability.
- Serves as a trusted advisor to executive leadership regarding AI strategy, organizational readiness, technology investments, implementation risks, governance considerations, and emerging industry developments.
- Prepares executive\-level presentations, strategic recommendations, implementation roadmaps, business cases, performance reporting, and status updates related to enterprise AI initiatives.
- Fosters a culture of innovation, experimentation, operational excellence, continuous improvement, and responsible AI adoption throughout the organization.
- Other duties as required.
Requirements:
- Strong understanding of artificial intelligence concepts and applied enterprise use cases, including generative AI, large language models, machine learning, intelligent automation, workflow orchestration, retrieval\-augmented generation, AI agents, and knowledge management systems is required.
- Demonstrated experience delivering AI\-enabled business solutions, digital transformation initiatives, automation programs, or enterprise technology implementations.
- Proven ability to identify, evaluate, prioritize, and implement business process improvements and transformation initiatives.
- Strong business analysis, product management, project leadership, change management, and stakeholder engagement skills.
- Experience facilitating cross\-functional initiatives involving business, technology, compliance, and executive stakeholders.
- Strong analytical, organizational, problem\-solving, and strategic planning capabilities.
- Experience with organizational change management, user adoption strategies, training, and communication planning.
- Ability to translate complex technical concepts into clear business recommendations and executive\-level communications.
- Familiarity with data governance, information security, privacy considerations, risk management, and responsible AI practices.
- Experience evaluating software vendors, enterprise technology platforms, implementation partners.
- Lean Mindset/Lean Thinking experience and approach is preferred.
- Experience with background screening, identity verification, or regulated information services is preferred.
- Proficiency in Microsoft Office applications is required; enterprise business systems, workflow automation platforms, analytics platforms, and collaboration tools is preferred.
- Education: Bachelor’s degree in business, operations, information technology, computer science, engineering, data analytics, product management, organizational leadership, or a related field is required. MBA or other advanced degree is preferred.
- Certifications or licensure: AI\-related certifications, Six Sigma certification, project management certifications (PMP, Agile, Scrum, Project Management, etc.) are highly preferred. If you do any work\-related driving while at Asurint, a driver’s license and ability to maintain a driving record that is satisfactory to the company’s liability insurance carrier is required.
- Years of relevant experience: Minimum of 8 years in business transformation, project management, operations management, consulting, technology implementation, process improvement, digital transformation, or a related field is required. Three years of experience leading AI, automation, digital transformation, or enterprise technology initiatives is strongly preferred.
- Years of leadership experience: Minimum of 3 years leading cross\-functional teams or direct reports is preferred.
Working Conditions
- The work environment involves everyday risks or discomforts that require normal safety precautions typical of offices, including the need for general safe workplace practices with office equipment and computers, avoidance of trips and falls, and observance of fire regulations.
- This position is performed remotely within the United States or in an office setting in the Cleveland, Ohio headquarters, although off\-site meetings in various settings may occur.
- Inside Asurint’s office environment, the noise level is usually quiet to moderate.
- In a remote setting, the employee is responsible for maintaining a safe and secure work environment, for arranging the off\-site workspace in an ergonomically sound manner, and for maintaining standard Internet speeds in order to work effectively.
- In a remote setting, the employee is required to ensure that all equipment and records that are the property of Asurint but have been relocated to the off\-site workplace, are maintained in a safe and secure manner and are used only for business purposes.
- The schedule is generally normal Eastern Time Zone business hours (unless otherwise communicated based on position or working location), although the employee may be required to perform work, attend meetings and events before or after normal workings hours, and occasionally on weekends and evenings.
- Some travel by personal automobile and a valid driver’s license may be required. Occasional overnight travel may be required.
- Ability to lift light objects (less than 20 pounds) and carry them short distances (20 feet or less) is required.
- The work environment involves everyday risks or discomforts that require normal safety precautions typical of offices, including the need for general safe workplace practices with office equipment and computers, avoidance of trips and falls, and observance of fire regulations.
Benefits
In exchange for your unique abilities, perspectives and teamwork, Asurint offers a competitive salary and an excellent benefit package \- with options you can select according to your needs \- which includes:
- Medical, dental and vision effective first day of employment
- 401(k) with employer match
- Paid time off
- 10 company\-paid holidays
- Employee Assistance Program
- Wellness Program
- Paid Bereavement
- Pet Bereavement
- Pet Insurance
- Volunteer time off
- Telecommuting Stipend
- Professional development programs
- Short\-term disability
- Company\-paid long\-term disability
- Company\-paid life insurance
- Flexible spending/health savings accounts
- Employee referral bonus
- Asurint is an equal opportunity employer. All applicants will be considered for employment without regard to race, color, religion, age, sex, national origin, disability status, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
Please note, the pay range represents a good faith estimate for the role at the time of posting. Final compensation will be within this range and determined based on factors such as experience, skills, qualifications and any unique requirements, or specialized skill sets relevant to the role. We are committed to maintaining equitable pay across employees in similar roles to ensure fair and consistent compensation practices.
Salary Context
This $115K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Asurint, 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 $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($150K) sits 16% below the category median. Disclosed range: $115K to $185K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Asurint AI Hiring
Asurint has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cleveland, OH, US. Compensation range: $185K - $185K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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