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Practice Systems & Applied AI: Analyst Information Services Department
Debevoise & Plimpton LLP is a premier law firm with market-leading practices, a global perspective, and strong New York roots. Our clients look to us to bring a distinctively high degree of quality, intensity, and creativity to resolve legal challenges effectively and cost-efficiently. We believe in hiring talented and dedicated individuals as members of our administrative and professional community. We draw on the strength of our culture and structure to deliver the best of our firm to our lawyers and clients through true collaboration.
We are seeking a Practice Systems & Applied AI Analyst to join our Information Services Department. This role is central to the firm’s efforts to evaluate, implement, and support cutting-edge artificial intelligence solutions. The ideal candidate will bring curiosity, creativity, and a strong sense of collaboration to advance the thoughtful adoption of AI within a leading law firm. The Practice Systems & Applied AI Analyst reports to the Practice Systems & Applied AI Manager and works closely with lawyers, analysts, AI engineers, and the Head of Practice Systems & Applied AI. This role is a full-time, hybrid position, based out of our New York office.RESPONSIBILITIES INCLUDE, BUT ARE NOT LIMITED TO:
- Collaborate with the Practice Systems & Applied AI Manager and Head of Practice Systems & Applied AI to identify, evaluate, and implement advanced legal AI solutions.
- Help lead engagements with attorneys and practice groups to identify, develop, and implement AI use cases.
- Design, test, and refine AI prompting strategies and legal workflow solutions.
- Engage attorneys and staff to drive the adoption of AI solutions.
- Lead change management efforts to foster adoption, building awareness, engagement, and confidence among attorneys and staff in integrating AI into their daily work.
- Leverage expertise in innovative AI tools, particularly in prompt and context engineering, to strengthen the firm’s AI capabilities and shape best practices.
- Evaluate and experiment with AI platforms, custom-built solutions, APIs, and integrations, providing feedback on performance, usability, and suitability for legal practice.
- Translate between legal and technical requirements, helping lawyers understand system capabilities while working with developers and vendors to optimize solutions.
- Contribute to training programs, workshops, and demonstrations that empower attorneys and staff to use AI effectively.
- Provide responsive support and thoughtful guidance to users, enhancing their experience and maximizing solution impact.
- Track emerging trends and create best practice guides for AI use in the legal industry.
- Maintain up-to-date knowledge of developments in AI and their application to law.
- Independently manage and execute special projects as assigned.
QUALIFICATIONS:
- Bachelor’s degree required; advanced degree preferred.
- Experience in a large law firm or professional services organization, in areas such as practice management, technology, legal project management, innovation, knowledge management, or application management.
- Demonstrated interest and expertise in artificial intelligence, particularly its use in the legal sector.
- Curious and solution-oriented mindset with a drive to explore new ideas and solve problems creatively.
- Flexibility to learn and adapt in a rapidly evolving field, building on strong analytical skills.
- Understanding of data privacy, information governance, and ethical considerations within law firms.
- Familiarity with knowledge management tools, document management systems, intranets, or collaboration platforms (preferred).
- Working knowledge of content management and web applications (e.g., SharePoint).
- Team player who thrives on collaboration and engaging with diverse groups of professionals.
REQUIREDSKILLS:
- Strong documentation skills (diagrams, user guides, support documentation).
- Good foundation in prompt and context engineering, with proven experience improving technologies and workflows to deliver innovative solutions and impactful results for lawyers.
- Strong interpersonal and communication skills, with a proven ability to collaborate with lawyers and staff across levels.
- Solid project management experience and comfort using work management tools.
- Exceptional analytical, problem-solving, reading, writing, and communication skills, with strong attention to detail.
- Ability to navigate seamlessly between technical teams and legal professionals.
- Basic understanding of application architecture, including API-based integrations.
PREFERRED SKILLS:
- Understanding of relational databases (SQL Server) is preferred.
- Basic knowledge of T-SQL and MS SQL Management studio.
- Basic understanding of coding and software design, to work on vibe-coding sessions.
TO APPLY:
A resume is required to apply for this position. Please tell us your salary requirements and where you saw this position posted. Send required materials to:
Human Resources
212.909.8310
Debevoise & Plimpton LLP is an equal opportunity employer. All qualified applicants will receive equal consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran or any other legally protected category in accordance with U.S. law.
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Salary Context
This $130K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% of the market. At Debevoise & Plimpton LLP, 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 $212,160 based on 13,809 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($160K) sits 25% below the category median. Disclosed range: $130K to $190K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Debevoise & Plimpton LLP AI Hiring
Debevoise & Plimpton LLP has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $105K - $300K.
Location Context
AI roles in New York pay a median of $221,000 across 3,262 tracked positions. That's 4% above the national 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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