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About This Role
Founded in 1920, Akerman is recognized as one of the nation’s premier law firms, with more than 700 lawyers across the United States.
Akerman LLP, an AmLaw 100 firm, is seeking a hands\-on Director of Artificial Intelligence to lead the design, development, and responsible deployment of AI systems across the firm. This is not a purely strategic or advisory position—we are looking for a builder. The successful candidate will have personally architected and shipped production AI applications, including agentic systems, can read and write code, and understands the practical realities of running AI on confidential, privileged data in a regulated professional\-services environment.
The Director will own the process of scoping designing agentic workflows and tools that retrieve, reason over, and act on information across heterogeneous sources, selecting and tuning the right models for each task, and doing so under rigorous security and compliance controls. This person will work shoulder\-to\-shoulder with attorneys, practice groups, knowledge management, security, and IT.
Core Responsibilities:
Design and ship agentic AI systems. Architect, build, and operate agentic AI applications; systems that plan, call tools, retrieve and act on information, and execute multi\-step workflows with appropriate human oversight. Build and maintain the orchestration layer (tool/function calling, multi\-agent coordination, memory, state management, retries, and guardrails), and integrate agents with firm systems via MCP (Model Context Protocol) servers and other tool interfaces. Define where agents operate autonomously versus where a human stays in the loop.
Build the data and tooling backbone. Develop production\-grade pipelines and tools that collect and process information from diverse sources including public and subscription websites, REST and streaming API feeds, MCP servers and feeds, email systems, document management systems, and SQL and vector databases. Own these systems end to end, including retrieval (RAG) architectures, evaluation, observability, and iteration.
Model selection and orchestration. Demonstrate working fluency with frontier foundation models (e.g., OpenAI, Anthropic Claude, Google) via API, as well as locally hosted open\-weight models (e.g., Llama, Mistral, Qwen). Make sound, cost\-aware decisions about which model and which agent design fit each use case, and route tasks accordingly.
Tune and operate open\-weight models. Hands\-on experience fine\-tuning, adapting (LoRA/PEFT), quantizing, and serving open\-weight models on firm\-controlled or private\-cloud infrastructure to meet specific practice and business needs—particularly for agentic tasks and where data sensitivity precludes sending information to third\-party APIs.
Protect privilege and prevent data leakage. Treat the protection of attorney\-client privilege, work product, and confidential client information as a first\-order design constraint, made more acute by agentic systems that take actions and traverse multiple data sources. Architect agents and pipelines to prevent data leakage to external model providers, constrain tool permissions and scope, avoid inadvertent waiver or spoliation of privilege, enforce data residency and retention requirements, and maintain clear audit trails of every agent action.
Security partnership. Work closely with the firm's Information Security, Research (KM) and IT teams to ensure all AI systems, especially autonomous agents with tool access meet the firm's security standards, client outside\-counsel guidelines, and audit requirements. Conduct or support AI risk assessments and threat modeling (including prompt injection, tool\-abuse, excessive\-agency, data\-exfiltration, and model\-supply\-chain risks), and lead vendor security reviews.
Governance and compliance. Work with the firm's Information Security team that is responsible for firm's AI governance framework, to keep the firm aligned with recognized standards for AI management systems (e.g., ISO/IEC 42001\) and applicable regulatory regimes.
Cross\-functional collaboration and enablement. Partner with firm management, practice groups and KM to identify high\-value use cases, translate legal workflows into agentic designs and technical requirements, and prioritize by ROI, feasibility, and risk. Design AI applications training for attorneys and staff, lead change management, and build a culture of responsible, confident AI adoption.
Vendor and platform evaluation. Evaluate legal\-AI vendors and agent platforms, distinguish substance from marketing, run pilots with measurable success criteria, and advise on build\-vs\-buy decisions.
Team leadership. Build, mentor, and manage a small team of engineers and/or AI specialists as the function grows.
Required Qualifications:
- Minimum 5 years of technology experience, including a minimum 3 years building and deploying production AI/ML applications. This must be practical, hands\-on experience, not solely academic or research background. Candidates should be prepared to discuss systems they have personally built.
- Demonstrated experience building agentic AI systems. Tool/function calling, multi\-step or multi\-agent orchestration, and integrating agents with external systems and data sources (including MCP).
- Strong software engineering skills, including Python, working with REST/streaming APIs, SQL and vector databases, and modern AI/LLM and agent frameworks.
- Hands\-on experience with RAG pipelines, embeddings, evaluation, and observability for LLM and agent applications.
- Working knowledge of utilizing open\-weight models, including on\-premises or private\-cloud GPU infrastructure.
- Demonstrated experience designing AI systems under strict security, privacy, and confidentiality constraints; familiarity with data\-leakage prevention, least\-privilege tool access, encryption, and audit logging.
- Working knowledge of AI governance and risk frameworks (e.g., ISO/IEC 42001, NIST AI RMF) and relevant data\-privacy regulation.
- Proven ability to collaborate effectively with security, IT, and non\-technical stakeholders, and to communicate technical concepts to attorneys and firm leadership.
Preferred Qualifications:
- Prior experience in a law firm, legal\-technology provider, or other regulated professional\-services or highly confidential environment.
- Understanding of attorney\-client privilege, work\-product doctrine, and the legal and ethical duties (e.g., ABA Model Rules and recent formal opinions on generative AI) that constrain how AI may be used in legal practice.
- Experience building MCP servers and designing evaluation/guardrail frameworks for autonomous agents.
- Familiarity with legal\-specific platforms and use cases (document review, contract analysis, legal research, drafting).
- Background that combines software/AI engineering with exposure to litigation, eDiscovery, or knowledge management.
- Degree in computer science, engineering, data science, or related field; advanced degree or JD a plus but not required in lieu of practical engineering experience.
Job Type: Full\-Time; Monday \- Friday
Salary Range: The firm’s anticipated hiring range for this position is $165,000 – $200,000 annually. Actual compensation will be determined based on a variety of factors, including position, geographic location, education, training, and experience.
Bonus: Discretionary holiday bonus
Benefits: Paid Time Off, Medical Insurance, Dental Insurance, Vision Insurance, Life Insurance, Disability Insurance, 401k Profit Sharing Plan, and Transportation Program
We offer a competitive compensation and benefits package. Please submit your resume, cover letter and salary requirements. EOE
\#LI\-LS1
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $165K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Akerman 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 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. Disclosed range: $165K to $200K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Akerman LLP AI Hiring
Akerman LLP has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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