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About This Role
Posted on August 10, 2026
Dentons Bingham Greenebaum is currently seeking an Enterprise Applications Manager to be based in our Indianapolis, IN, Louisville, KY, or Cincinnati, OH office. The Enterprise Applications Manager is responsible for the strategy, lifecycle management, modernization, integration, and reliable operation of the Firm's enterprise application portfolio. The role supports legal, financial, document management, collaboration, workflow, endpoint, and productivity platforms in a secure hybrid cloud environment. This leader manages and develops application and endpoint technology professionals, partners with attorneys, practice groups, business services teams, IT leadership, vendors, and regional or global support teams, and translates business needs into scalable, secure, well\-governed technology solutions. The role places a strong emphasis on AI\-enabled productivity, platform modernization, process automation, data governance, adoption, measurable business value, and continuous improvement while protecting Firm confidentiality, client service standards, and operational resilience.
Essential duties and responsibilities
- Lead the enterprise applications strategy and roadmap in partnership with the Director of Technology, aligning application investments with attorney productivity, client service, security, compliance, operational efficiency, and Firm business objectives.
- Manage the day\-to\-day operation, support, enhancement, and lifecycle of core enterprise platforms, including Microsoft 365, Exchange Online, Azure Virtual Desktop, Microsoft Intune/Endpoint Manager, SharePoint, iManage Work, financial systems, workflow platforms, desktop applications, and approved legal technology tools.
- Directly supervise, coach, and develop Enterprise Applications and Endpoint team members, fostering accountability, documentation discipline, service ownership, cross\-training, and a collaborative support culture.
- Serve as a senior subject matter expert and escalation point for enterprise application issues, providing Tier 3 support and coordinating timely escalation to vendors, regional teams, or global support teams when required.
- Identify, prioritize, and deliver process automation opportunities across legal and business workflows using approved automation platforms, low\-code or no\-code tools, APIs, scripting, workflow engines, and integration services.
- Evaluate, pilot, implement, and govern responsible AI capabilities, including Microsoft 365 Copilot, Copilot Studio agents, prompt libraries, retrieval\-based knowledge experiences, and AI\-enabled legal or business applications.
- Partner with attorneys, practice groups, Knowledge Management, Training, Finance, Records, Information Governance, Security, and business services leaders to understand workflows, map requirements, define use cases, and implement practical solutions that improve adoption and reduce manual effort.
- Establish and maintain application governance practices, including change management, release planning, testing, documentation, access controls, licensing, support models, adoption plans, usage metrics, and retirement plans.
- Support responsible AI and data governance by coordinating with Security, Compliance, Information Governance, and Risk teams on confidentiality, permissions, sensitivity labels, data loss prevention, auditability, human review, and appropriate use standards.
- Lead application implementations, upgrades, migrations, integrations, and production deployments, including communication plans, maintenance windows, validation steps, rollback planning, and post\-implementation support.
- Oversee enterprise integrations and data flows among legal, financial, document management, workflow, collaboration, and reporting systems, ensuring reliability, security, maintainability, and alignment with Firm architecture standards.
- Monitor application performance, incidents, recurring issues, release notes, vendor roadmaps, and user feedback to identify improvements, reduce risk, and enhance service quality.
- Develop and maintain playbooks, knowledge articles, technical specifications, support procedures, training materials, and user\-facing guidance for enterprise applications, AI tools, and automated workflows.
- Define and report operational and business\-value metrics, including adoption, usage, service trends, incident patterns, automation impact, licensing utilization, and value realization for key platforms.
- Evaluate emerging legal technology, AI, automation, document management, collaboration, and workflow solutions, making build\-versus\-buy recommendations and business cases that consider cost, risk, security, user experience, and long\-term maintainability.
- Manage vendor relationships, software licensing, renewals, contract inputs, budget recommendations, roadmap discussions, and service reviews for assigned enterprise application platforms.
- Assist with departmental planning, budgeting, audits, business continuity testing, disaster recovery planning, and risk mitigation activities for applications and services.
- Coordinate with the Training Department and communications stakeholders to plan change management, adoption campaigns, demonstrations, office hours, and training for new or enhanced applications.
- Adhere to all IT policies and procedures, including architecture, security, privacy, disaster recovery, purchasing, records, information governance, acceptable use, and service management standards.
- Perform other duties and special projects as assigned by the Director of Technology.
Qualifications
- Bachelor's degree in Information Technology, Computer Science, Information Systems, Business Administration, or a related field preferred; equivalent education and relevant experience may be considered.
- Eight or more years of progressive experience supporting enterprise applications in a complex professional services, legal, financial, or similarly regulated environment; law firm experience preferred.
- Three or more years of experience leading, supervising, mentoring, or developing technical professionals, with demonstrated ability to build accountable, service\-oriented teams.
- Demonstrated experience leading enterprise application implementations, upgrades, migrations, integrations, production deployments, and cross\-functional technology projects.
- Hands\-on knowledge of Microsoft 365, Exchange Online, SharePoint, Teams, Microsoft Intune/Endpoint Manager, Azure Virtual Desktop, identity and access concepts, endpoint management, and enterprise desktop applications.
- Experience with legal technology platforms such as iManage Work, Intapp, Elite 3E or similar financial systems, document management, records, workflow, time entry, or practice support applications preferred.
- Working knowledge of AI\-enabled productivity tools, generative AI concepts, prompt engineering, Copilot or comparable enterprise AI tools, agent lifecycle management, retrieval\-based experiences, and responsible AI governance preferred.
- Experience identifying and automating business processes using tools such as Power Automate, Power Apps, scripting, APIs, integration platforms, business process management tools, or comparable automation technologies.
- Strong understanding of application lifecycle management, change management, incident and problem management, release management, documentation, licensing, vendor management, and service delivery best practices.
- Ability to understand legal and business workflows, identify practical technology improvements, translate requirements into actionable plans, and communicate technical concepts in business\-friendly and user\-friendly language.
- Strong judgment regarding confidentiality, data security, permissions, privacy, information governance, risk management, and professional responsibility considerations in a law firm environment.
- Excellent analytical, troubleshooting, project management, prioritization, documentation, and problem\-solving skills.
- Demonstrated ability to manage competing priorities, deliver under deadlines, and balance innovation with stability, security, supportability, and user experience.
- Strong interpersonal, facilitation, presentation, and stakeholder management skills, including the ability to work effectively with attorneys, executives, vendors, IT colleagues, and non\-technical users.
- Highly self\-motivated, curious, service\-oriented, detail\-focused, and committed to continuous learning, platform modernization, process automation, and operational excellence.
Preferred certifications
- Microsoft certifications such as Azure Fundamentals, Azure Administrator, Microsoft 365 Administrator, Microsoft 365 Certified: Endpoint Administrator, Security/Compliance, Power Platform, or Copilot\-related certifications.
- ITIL Foundation or equivalent service management certification.
- AI or data\-related certifications such as Microsoft Azure AI Fundamentals, Azure AI Engineer Associate, or comparable responsible AI, automation, or analytics credentials.
- Relevant legal technology, document management, financial system, or vendor platform certifications preferred.
Working conditions
- Normal law office environment with little exposure to excessive noise, dust, temperature, or similar conditions.
- Flexible, hybrid work environment, subject to Firm policy and business needs.
- Position may require occasional work outside standard business hours to support maintenance windows, upgrades, deployments, incident response, business continuity testing, or critical Firm needs.
- Occasional travel to other offices, vendor meetings, conferences, or training sessions may be required.
- Dexterity of hands and fingers to operate a computer keyboard, mouse, tools, and other computer components.
- Occasional lifting and transport of moderately heavy objects, such as computers, peripherals, or related equipment.
Equal opportunity and accommodation
The Firm is committed to equal employment opportunity and will consider reasonable accommodations consistent with applicable law and Firm policy.
*The above is intended to describe the general content of and requirements for the performance of this job. It is not to be construed as an exhaustive statement of essential functions, responsibilities, or requirements. Duties, responsibilities, and requirements may change as business needs evolve.*
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 Dentons, 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 $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.
Dentons AI Hiring
Dentons has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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