Interested in this AI/ML Engineer role at CARR ALLISON?
Apply Now →About This Role
Carr Allison is a well\-regarded civil litigation firm, boasting a team of over 150 attorneys across the Southeast. Our unique philosophy of "early identification – early resolution" has earned us decades of trust and respect from our clients. At Carr Allison, our success is fueled by the collective efforts of our team, and we believe that an open\-door policy is essential to our continued growth. We proudly champion teamwork, collaboration, and the pursuit of excellence as the cornerstones of our firm's culture. Join a team where your growth is our priority and your work truly matters.
We are currently hiring for a Legal Applications \& AI Adoption Analyst. This position is responsible for supporting, improving, and promoting the firm's use of key business applications and approved artificial intelligence platforms. This role combines traditional legal applications support with a strong focus on accelerating attorney and staff adoption of AI tools, including Harvey and Aderant AskMaddie.
The position serves as a bridge between IT, attorneys, practice groups, administrative departments, and vendors. The analyst will help users understand how to effectively use firm\-approved applications, identify opportunities for automation and process improvement, develop training materials, and provide hands\-on support for legal technology solutions.
This is a remote\-eligible position and reports directly to the Chief Information Officer. The employee must be able to effectively support users across multiple offices, participate in virtual meetings and training sessions, maintain regular communication with the CIO and IT team, and travel occasionally if needed for firmwide training, major projects, or office\-specific support.
Responsibilities include:
- Support the firm's adoption and effective use of approved AI platforms, including Harvey and Aderant AskMaddie.
- Provide one\-on\-one and group training to attorneys and staff on approved AI tools.
- Develop practical AI use cases tailored to legal workflows, administrative tasks, and firm operations.
- Help attorneys and staff understand appropriate, secure, and effective ways to use AI in daily work.
- Create quick reference guides, sample prompts, workflow examples, and training materials.
- Monitor AI usage trends and assist with adoption reporting, user engagement, and follow\-up training.
- Identify barriers to AI adoption and recommend solutions to improve usage and confidence.
- Coordinate with vendors on AI\-related support issues, product updates, training opportunities, and roadmap items.
- Assist in evaluating new AI features or tools for possible firm use.
- Reinforce firm policies regarding confidentiality, client data, ethical AI use, and responsible technology adoption.
- Provide application support for core firm systems, including but not limited to:
+ Aderant
+ iManage
+ Microsoft 365
+ Microsoft Power Automate
+ Foxit PDF Editor\+
+ Other legal, productivity, document management, and workflow tools
- Troubleshoot application issues reported by attorneys, staff, and administrative departments.
- Work with vendors and internal IT team members to resolve application\-related problems.
- Assist with application configuration, user access, security roles, templates, workflows, and integrations.
- Support application upgrades, testing, deployments, and documentation.
- Maintain knowledge of firm workflows and recommend improvements using existing technology.
- Assist with reporting, data validation, and process analysis related to firm applications.
- Develop user\-friendly documentation, training guides, FAQs, short videos, and tip sheets.
- Conduct live and remote training sessions for attorneys, paralegals, assistants, and administrative staff.
- Create role\-specific training content for legal applications and AI tools.
- Partner with practice groups and departments to identify recurring pain points and training needs.
- Help standardize best practices for use of the firm's legal technology tools.
- Promote awareness of underutilized application features that can improve productivity.
- Assist with onboarding new attorneys and staff on core firm applications.
- Identify opportunities to streamline manual or repetitive processes using tools such as Microsoft Power Automate.
- Assist in designing, testing, documenting, and supporting basic workflow automations.
- Work with users to understand business requirements and translate them into practical technology solutions.
- Support continuous improvement initiatives involving document workflows, approvals, notifications, reporting, and task automation.
- Collaborate with IT team members on integrations between Microsoft 365, Aderant, iManage, and other systems where appropriate.
- Coordinate with application vendors for support, troubleshooting, training, and product information.
- Participate in application\-related projects, upgrades, implementations, and pilots.
- Track issues, enhancement requests, and recurring user concerns.
- Assist with testing and validation of new features, patches, and releases.
- All other tasks assigned by supervisor and/or Chief Operating Officer
Required Qualifications:
- Experience supporting business, legal, or enterprise applications.
- Strong understanding of Microsoft 365 applications, including Outlook, Word, Excel, Teams, OneDrive, and SharePoint.
- Strong communication skills with the ability to work effectively with attorneys, staff, vendors, and IT personnel.
- Ability to explain technical concepts in clear, practical, and non\-technical language.
- Strong troubleshooting and problem\-solving skills.
- Ability to create clear documentation, training materials, and user guides.
- Ability to work independently in a remote environment.
- Strong organizational skills and ability to manage multiple priorities.
- Candidates must work in one of the states where the firm operates: Alabama, Georgia, Florida, Mississippi, or Tennessee.
Preferred, but not required:
- Experience in a law firm or professional services environment.
- Experience with Aderant, iManage, Harvey, Microsoft Power Automate, Foxit, or Kofax Power PDF.
- Familiarity with AI tools used in legal or professional services environments.
- Experience creating and delivering training to attorneys, paralegals, legal assistants, or administrative teams.
- Experience with workflow automation, reporting, or process improvement.
- Familiarity with legal confidentiality, client data protection, and ethical technology use.
- Basic understanding of SQL, reporting tools, or data analysis is a plus.
Office Culture:
- Strong, close\-knit team with a supportive and easy\-going environment.
- Emphasis on teamwork, accountability, and continuous improvement.
Please send your resume and salary requirements to [email protected]. Be part of a firm that values your potential and empowers your success.
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 CARR ALLISON, 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.
CARR ALLISON AI Hiring
CARR ALLISON has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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