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Krieg DeVault LLP is seeking a visionary and highly collaborative Director of Artificial Intelligence to lead the Firm's AI strategy, adoption, and value realization efforts. This newly created leadership role will serve as the central force driving the practical integration of AI across our legal and business operations.
Reporting directly to the Chief Operating Officer and partnering closely with Firm leadership, Information Technology, Information Resources, and Practice Groups, the Director of Artificial Intelligence will help transform how attorneys and staff work by embedding AI into daily workflows, developing meaningful use cases, and ensuring measurable business impact from AI investments.
This position serves in a non\-technical capacity. Rather, we are seeking a leader who can bridge technology and legal practice, helping our professionals confidently adopt and utilize AI to enhance efficiency, service delivery, and innovation.
Essential Duties and Responsibilities:
AI Strategy \& Governance
- Provide centralized leadership and coordination for AI across the Firm, ensuring alignment with Firm priorities and business objectives;
- Serve as the Firm’s AI value realization leader, ensuring technology investments translate into practical application, workflow integration, and measurable outcomes;
- Align AI initiatives with Firm policy, risk management requirements, and ethical standards;
- In coordination with the General Counsel and the Professional Standards Committee, reinforce verification and quality control expectations in the use of AI tools;
- Define, track, and report on key success metrics including adoption, workflow integration, and measurable business impact;
- Establish and execute against short\- and long\-term milestones that drive firmwide adoption and value realization.
Training \& Adoption
- In concert with Firm leadership, develop and implement practice\-specific, role\-based training programs for attorneys and staff aligned to real legal workflows;
- Provide practical guidance on development and use of agents, prompting, tools, and workflow application;
- Drive a firmwide culture shift toward AI as a standard component of daily work, moving from optional use to consistent and expected adoption;
- Reinforce expectations for responsible and effective AI use aligned with Firm policy, quality standards, and professional obligations;
- Own firmwide adoption strategy and accountability to drive consistent, scaled usage across Practice Groups and business functions.
Workflow Integration
- Embed AI into core Firm processes including iManage, document workflows, Outlook, time entry, and other key systems;
- Translate AI capabilities into practical, repeatable workflows that integrate seamlessly into daily work;
- Ensure AI is embedded into core processes rather than used as a stand\-alone tool;
- Identify and address gaps in practice\-specific workflow integration to ensure AI delivers meaningful value within legal work.
Use Case Development \& Enablement
- Develop and scale practice\-specific AI use cases across Practice Groups;
- Create structured, practical guidance addressing “what to use, when, and how”;
- Translate firmwide AI capabilities into practice\-level applications aligned to legal workflows and client service delivery;
- Partner with internal resources to test, validate, and refine use cases;
- Reduce tool confusion and knowledge fragmentation through consistent, accessible resources.
Cross\-Functional Coordination
- Coordinate AI efforts across Information Technology, Information Resources, and Practice Groups to ensure alignment and consistent execution;
- Serve as a core member and AI liaison to key committees, including the Innovation Committee, providing leadership, alignment, and direction on AI initiatives;
- Lead internal and external AI task forces to ensure effective execution, knowledge sharing, and alignment with Firm priorities;
- Maintain awareness of external market trends, emerging AI practices, and client expectations to inform Firm strategy and positioning.
Client \& Talent Alignment
- Support client communication and transparency regarding AI use aligned with client expectations and Firm standards;
- Support development of attorneys and staff in an AI\-enabled environment, adapting training and workflows to evolving expectations.
Future Capability and Scaling
- Lead the development and execution of a phased AI maturity roadmap, evolving the Firm from foundational adoption to scaled integration and advanced capability;
- Partner with Firm leadership to assess and recommend future AI roles, resources, and capabilities aligned with adoption maturity and business needs;
- Develop and scale internal enablement structures, including AI Champions, to support consistent adoption and knowledge sharing;
- Advance the use of automation, agents, and emerging capabilities to deepen AI integration into legal workflows over time.
Investment and Planning
- Provide input into AI investment planning, including tools, resources, and talent required to support Firm objectives;
- Support leadership in maintaining a cost\-disciplined, value\-driven approach to AI investments.
- Align AI initiatives with value delivery and pricing considerations in a legal environment;
- Measure and demonstrate return on investment (ROI) of AI initiatives through efficiency, consistency, and value delivery outcomes.
Qualifications and Competencies
The requirements listed below are representative of the knowledge, skill, and/or ability required.
- Strong technical fluency and understanding of AI tools and capabilities, including experience with development and use of AI agents;
- Demonstrated leadership, communication, and change management capabilities;
- Ability to translate technology into practical legal and business workflows;
- Experience working in professional services environments, preferably law firms;
- Ability to work effectively across multiple functions and stakeholder groups;
- Strong organizational and multitasking skills with the ability to manage competing priorities;
- Excellent communication and interpersonal skills with the ability to engage professionally across all levels of the Firm;
- Strong attention to detail and follow\-through;
- Ability to handle confidential and sensitive information with discretion and sound judgment;
- Experience developing presentations, training materials, process documentation, or knowledge resources;
- Self\-starter capable of working independently while remaining aligned with Firm priorities and leadership direction;
- Ability to translate technical or complex concepts into practical guidance for non\-technical audiences;
- Strong analytical thinking, professionalism, and operational judgment.
Work Environment \& Expectations
- On\-site presence required to support daily office operations;
- Requires routine and regular collaboration with attorneys, administrative leadership, and cross\-functional teams;
- The role requires the ability to manage multiple initiatives, coordinate across the Firm, and drive adoption of new workflows and practices.
*EEO Policy: It is the policy of Krieg DeVault LLP that an individual’s race, color, religion, sex, disability, sexual orientation, gender identity, U.S. military veteran status, national origin, age, genetic information, family status, or other characteristics protected by law are not and will not be considered in any personnel or management decisions. We affirm our commitment to these fundamental policies.*
*E\-Verify: Krieg DeVault LLP participates in the federal government’s E\-Verify program. With all new hires, we provide the Social Security Administration and, when applicable, the U.S. Department of Homeland Security with information from each new employee’s Form I\-9 to confirm work authorization.*
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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 Krieg DeVault 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
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.
Krieg DeVault LLP AI Hiring
Krieg DeVault LLP has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Indianapolis, IN, US.
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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