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About This Role
We are a Firm where people truly believe in what they do and strive to achieve the highest standards of performance and success.
*This position can be based in our global operations center in Tampa, FL, or at one of the Firm's offices: Atlanta, Austin, Birmingham, Boston, Century City, Charlotte, Chattanooga, Chicago, Dallas, Denver, Fort Lauderdale, Houston, Jacksonville, Los Angeles, Miami, Nashville, Newport Beach, New York, Orlando, Philadelphia, Portland, Richmond, San Francisco, Seattle, Stamford, Tallahassee, Tysons, Washington, D.C., or West Palm Beach.*
General Description:
The AI Legal Engineer is responsible for partnering with subject matter experts to design, build, and operationalize AI\-enabled workflows that solve concrete problems for the Firm's practice groups, and for equipping attorneys to build, refine, and govern their own workflows over time.
This role pairs hands\-on workflow or skill development including prompt design, retrieval pipelines, document templates, evaluations, and guardrails with practice\-group\-facing training, enablement, and change management. The AI Legal Engineer will serve as a primary, trusted resource for our practice groups, supporting the successful rollout and sustained adoption of AI solutions and driving meaningful, long\-term behavior change.
The AI Legal Engineer works closely with the AI Product team, AI Adoption team, the broader Knowledge \& Innovation team, IT, the Innovation Practice Group, and Professional Development to translate attorney needs into reliable, repeatable AI solutions that deliver measurable value in daily legal work.
Key Responsibilities and Essential Job Functions:
- Design, build, test, and deploy AI legal workflows and skills across firm\-approved AI tools, supporting use cases such as contract review, due diligence, drafting, research, and summarization.
- Partner directly with attorneys and knowledge management professionals to identify high\-value use cases, map workflows, and translate practice needs into structured AI solutions.
- Develop and maintain retrieval pipelines, document templates, and reusable workflow components with clear, attorney\-ready documentation.
- Build and execute evaluation frameworks to measure workflow quality, accuracy, and reliability; monitor performance and iterate based on feedback and outcomes.
- Serve as a go\-to resource for enablement and change management for developed workflows, partnering with the AI Adoption team to drive usage, deliver training sessions and workshops, 1:1 coaching, and develop practical playbooks and materials that support sustained adoption.
- Develop and scale best\-practice standards, use\-case libraries, and playbooks that empower attorneys to responsibly and independently leverage AI tools while maintaining quality and risk controls.
- Embed human\-review checkpoints and audit mechanisms aligned with Firm governance, confidentiality, and professional responsibility requirements.
- Collaborate closely with cross\-functional stakeholders including the AI Product team, AI Adoption team, Knowledge \& Innovation team, IT, Innovation Practice Group, and Professional Development to ensure aligned delivery and support.
- Track adoption, usage, and impact metrics for deployed workflows and recommend refinements or expansion opportunities.
- Manage ongoing maintenance of production workflows, including updates to prompts, retrieval sources, and guardrails in response to model and platform changes, user feedback, or evolving practice needs.
- Represent the AI Products \& Adoption team in internal meetings, committees, and practice group sessions.
- Expected to maintain a regular and predictable work schedule and full attention to and engagement in work activities on behalf of the firm during business hours unless otherwise approved or required by applicable law.
- Special projects and duties as assigned.
Required Skills:
- Demonstrated ability to design and build AI\-enabled workflows in legal or professional\-services settings, including prompt engineering, retrieval\-augmented generation (RAG) concepts, and evaluation of model outputs.
- Working understanding of legal practice and attorney workflows, with the judgment to identify where AI is, and is not, appropriate.
- Strong interpersonal skills with experience building trusted relationships with attorneys, including partners and senior stakeholders.
- Clear, confident communicator able to teach AI concepts and workflow\-building skills to non\-technical legal audiences in practical terms.
- Comfort iterating in ambiguity: scoping a use case, building a prototype, gathering feedback, and refining toward a production\-ready workflow.
- Strong attention to quality, accuracy, and risk; instinct to build in human\-review and verification steps for legal output.
- Consistently reliable, responsive, and solutions oriented.
- Working knowledge of the legal AI vendor landscape (e.g., Harvey, Legora, Microsoft Copilot, and similar) and familiarity with the components of modern AI workflow builders.
- Proficient in Microsoft Office Suite, or Microsoft 365\.
Required Qualifications \& Education:
- Juris Doctor (JD) or equivalent legal background (e.g., practicing attorney, knowledge lawyer, paralegal with substantive practice experience)
- 3–6\+ years of experience in legal, legal technology, knowledge management, practice innovation, or a comparable professional\-services environment.
- Demonstrated experience designing or implementing AI, automation, or workflow solutions in a legal or professional\-services context.
- Experience delivering training, enablement, or onboarding programs to attorney or client\-facing audiences.
Preferred Qualifications \& Education:
- Prior experience at an AmLaw firm, in\-house legal department, or legal technology company (e.g., Harvey, Legora, or similar).
- Hands\-on experience with workflow builders, prompt libraries, RAG pipelines, and AI evaluation methods.
- Familiarity with AI governance frameworks and emerging AI regulation relevant to legal practice.
- Light coding fluency (e.g., Python, SQL, or scripting) a plus but not required.
Physical Requirements:
- Ability to sit or stand for extended periods of time.
- Moderate or advanced keyboard usage.
This position may be filled in multiple locations. In accordance with applicable Pay Transparency Laws, the pay range(s) for this position are listed below. These ranges may not be applicable to other locations. An individual's actual compensation will depend on the individual's qualifications and experience. In addition to the base compensation, Holland \& Knight provides bonus opportunities and an exceptional benefits package. California, Massachusetts, New York (City), District of Columbia: $163,000\.00 \- $245,000\.00 per year Illinois, Washington, Tysons: $150,000\.00 \- $224,000\.00 per year Colorado, Richmond: $136,000\.00 \- $204,000\.00 per yearBenefits: Our goal is to promote a work environment in which individuals have access to the resources they need to be their best both professionally and personally, which includes resources that encourage individuals to focus on their health and well\-being.
Below are the benefits we offer: comprehensive medical (PPO and HDHPs), dental and vision plans including coverage for domestic partners; life and AD\&D insurance; short and long term disability insurance; tax\-advantaged accounts for health care expenses, including FSAs and HSAs; FSAs for dependent care; health advocacy services; behavioral health and counseling resources for all family members; 401(k); profit sharing; backup dependent care; senior care planning support; resources for individuals with development disabilities and their caregivers; and paid holidays and other paid time off, including paid leave for new parents.
*Holland \& Knight is an Equal Opportunity Employer and does not discriminate on the basis of race, color, religion, sex (including pregnancy, childbirth or related conditions, transgender status, and sexual orientation), national origin, age, disability, genetic information, veteran status or any other factor prohibited by law.*
*Applicants who are interested in applying for a position and require an accommodation during the process should contact* *ApplicantAccommodations@hklaw.com**.*
*Personal Information collected from applicants will be used for the purpose of processing the application throughout any recruitment or employment process, as well as inclusion in a personnel file. Categories of data collected may include name, address, phone numbers, email, Social Security Number, and signature. Holland \& Knight may collect further information if you consent to a background check. This includes criminal background, employment, and certifications. Please visit* *Legal Information* *Portal for Holland \& Knight LLP’s privacy policies.*
Salary Context
This $136K-$245K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Holland & Knight, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($190K) sits 13% below the category median. Disclosed range: $136K to $245K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Holland & Knight AI Hiring
Holland & Knight has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Brandon, FL, US. Compensation range: $245K - $245K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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