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About This Role
About Acrisure
A global fintech leader, Acrisure empowers millions of ambitious businesses and individuals with the right solutions to grow boldly forward. Bringing cutting-edge technology and top-tier human support together, we connect clients with customized solutions across a range of insurance, reinsurance, payroll, benefits, cybersecurity, mortgage services – and more.
In the last twelve years, Acrisure has grown in revenue from $38 million to almost $5 billion and employs over 19,000 colleagues in more than 20 countries. Acrisure was built on entrepreneurial spirit. Prioritizing leadership, accountability, and collaboration, we equip our teams to work at the highest levels possible.
Job Summary:
Acrisure is seeking a Legal AI & Automation Analyst to join our team in Grand Rapids, MI.
Following a successful pilot and initial adoption of Harvey AI within the Legal Department, Acrisure is scaling its investment in legal AI and expanding these capabilities across the enterprise. Reporting to the Program Manager, the Technology Analyst will be a hands-on builder focused on the technical build-out of AI-powered workflows, working directly with teams to turn everyday processes into repeatable efficiencies. This role will help hyperscale adoption within Legal and extend proven solutions to other departments, adding the capacity needed to realize the full value of AI at Acrisure.
Responsibilities:
- Design, build, test, and deploy repeatable Harvey AI solutions, including Workflow Agents, Review Tables, and Playbooks, that streamline high-volume daily work for legal teams.
- Partner directly with attorneys, paralegals, and legal staff to identify repetitive, time-intensive tasks and translate them into structured, automated AI workflows.
- Validate the accuracy, consistency, and quality of AI-generated outputs before release, and continuously refine solutions based on user feedback and performance results.
- Support the expansion of Harvey AI beyond the Legal Department to functions such as Regulatory Affairs, Risk, Equity Administration, Compliance, Sourcing, and Human Resources, adapting proven capabilities including template filling, large-scale document data extraction, contract analysis, document drafting and editing, and jurisdictional alignment.
- Facilitate ideation sessions with teams to uncover high-value use cases, then lead the technical build-out once teams are trained and familiar with the platform.
- Deliver user training, onboarding, and day-to-day technical support to drive adoption and confident, consistent use of AI tools across the organization.
- Track usage and adoption data, help quantify efficiency gains and cost savings, and contribute to program reporting for leadership.
- Maintain clear documentation, build standards, and a library of reusable workflows, prompts, and templates to support scalable, consistent deployment.
- Follow and help reinforce program governance, data privacy, and confidentiality standards in all solutions built and deployed.
- Stay current on Harvey product releases and emerging legal AI capabilities, including Long Horizon Agents, and recommend opportunities to apply new functionality.
- Enterprise software launches, integration, and scaling across various workstreams and teams.
Required Qualifications:
- Hands-on experience building solutions with generative AI tools (e.g., Harvey, Microsoft Copilot, ChatGPT, or Claude), including advanced prompt design and iterative prompt refinement.
- Experience designing, configuring, or automating multi-step workflows using AI platforms, low-code/no-code tools, or similar technologies (e.g., Microsoft Power Automate or Copilot Studio).
- Strong technical aptitude and the ability to quickly learn new platforms, features, and capabilities as AI technology evolves.
- Experience testing and validating technology outputs for accuracy, consistency, and reliability, with meticulous attention to detail.
- Strong analytical and problem-solving skills, with the ability to break down complex business processes into clear, repeatable steps.
- Excellent written and verbal communication skills, including the ability to explain technical concepts to non-technical audiences.
- Ability to manage multiple projects and competing priorities in a fast-paced environment while handling confidential information with discretion.
- Advanced proficiency in Microsoft 365 (Excel, Word, Teams, SharePoint).
Preferred Qualifications:
- Direct experience building in Harvey AI, including Workflow Agents, Review Tables, or Playbooks.
- Familiarity with AI concepts such as large language models (LLMs), retrieval-augmented generation (RAG), AI agents, and structured data extraction.
- Data analysis and reporting skills using tools such as Excel, Power BI, or Tableau.
- Understanding of responsible AI practices, AI governance, and data privacy considerations.
- Experience creating training materials or delivering end-user training.
- Familiarity with legal documents, contracts, or legal technology platforms (e.g., contract lifecycle management or document management systems).
- Experience with scripting, data, or integration tools (e.g., Python, SQL, or APIs).
Education and Experience:
Required Qualifications
- Bachelor's degree in Information Systems, Computer Science, Data Analytics, Information Science, Legal Studies, Business Administration (with a technology, analytics, or operations focus), or a related field, or equivalent practical experience.
- 2+ years of experience in a technical, analytical, or AI-focused role, such as business systems analyst, automation specialist, solutions analyst, or technology implementation.
- Demonstrated experience implementing or supporting the adoption of a technology solution with end users.
Preferred Qualifications
- Certifications in AI, automation, or related platforms (e.g., Microsoft AI or Power Platform certifications) or in process improvement (e.g., Lean Six Sigma).
- Prior involvement in an AI tool pilot, rollout, or enterprise-wide adoption program.
- Experience supporting legal, compliance, risk, or other highly regulated functions, ideally within financial services, insurance, or fintech.
- Legal operations or paralegal experience is a plus but not required.
#LI-hybrid
Candidates should be comfortable with an on-site presence to support collaboration, team leadership, and cross-functional partnership.
Why Join Us:
At Acrisure, we’re building more than a business, we’re building a community where people can grow, thrive, and make an impact. Our benefits are designed to support every dimension of your life, from your health and finances to your family and future.
Making a lasting impact on the communities it serves, Acrisure has pledged more than $22 million through its partnerships with Corewell Health Helen DeVos Children's Hospital in Grand Rapids, Michigan, UPMC Children's Hospital in Pittsburgh, Pennsylvania and Blythedale Children's Hospital in Valhalla, New York.
Employee Benefits
We also offer our employees a comprehensive suite of benefits and perks, including:
- Physical Wellness: Comprehensive medical insurance, dental insurance, and vision insurance; life and disability insurance; fertility benefits; wellness resources; and paid sick time.
- Mental Wellness: Generous paid time off and holidays; Employee Assistance Program (EAP); and a complimentary Calm app subscription.
- Financial Wellness: Immediate vesting in a 401(k) plan; Health Savings Account (HSA) and Flexible Spending Account (FSA) options; commuter benefits; and employee discount programs.
- Family Care: Paid maternity leave and paid paternity leave (including for adoptive parents); legal plan options; and pet insurance coverage.
- … and so much more!
This list is not exhaustive of all available benefits. Eligibility and waiting periods may apply to certain offerings. Benefits may vary based on subsidiary entity and geographic location.
Acrisure is an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, religion, sex, national origin, disability, or protected veteran status. Applicants may request reasonable accommodation by contacting [email protected].
Final candidates will be required to complete post-offer verification processes related to the role and in accordance with applicable laws.
California Residents: Learn more about our privacy practices for applicants by visiting the Acrisure California Applicant Privacy Policy.
Recruitment Fraud: Please visit here to learn more about our Recruitment Fraud Notice.
Welcome, your new opportunity awaits you.
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 Acrisure LLC, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
Acrisure LLC AI Hiring
Acrisure LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Grand Rapids, MI, US.
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.
Frequently Asked Questions
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