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About This Role
AI Transformation Lead
Intapp's Enterprise Transformation and AI Center of Excellence is responsible for turning frontier AI capabilities, Celeste and Claude, into production workflows that our own GTM and Finance teams rely on every day. The AI Transformation Lead leads that work end to end: scoping the business problem, designing the agent or integration, shipping it into a live system such as OP4i, and proving out the impact with real usage data.
This is a hands\-on delivery role, not an evangelism or training function. You will own the full path from concept to production for our highest\-value agentic workflows, working inside a defined governance model that includes structured prioritization, UAT, and change management. You will also be responsible for turning what you learn into reusable playbooks and frameworks that make the next deployment faster than the last.
You'll work closely with Business Transformation Managers, Data Quality Manager, Operations, G\&A, IT, and Applied AI and you'll have a direct role on the AI Advisory Council that sets investment and prioritization decisions for the enterprise AI program.
Adoption
- Consistent weekly active usage of deployed workflows across the target function, measured against licensed seats
- New workflows reach broad usage within 90 days of general availability
Impact
- Measurable time savings per workflow against a documented baseline, not an estimate
- Clear improvement in data quality, cycle time, forecast accuracy, client NPS, etc. tied directly to your deployment
Maturity
- A growing portfolio of agentic workflows moved from concept to production each quarter
- Deployment patterns codified into playbooks and frameworks that other teams can reuse
- A shrinking concept\-to\-production cycle time as your patterns mature
What You Will Do
- Lead end\-to\-end delivery of AI\-driven workflows, from intake and prioritization through build, UAT, and production rollout
- Embed directly with Sales, Services, Success \& Support, etc. to identify and scope real workflow friction
- Work directly with Applied AI team to bring client successes and learnings to inform the internal Celeste workflow roadmap; additionally, share internal successes with the Applied AI team
- Design and build integrations \& workflows across OP4I, M365, Celeste, Claude, etc. including new, Celeste Playbooks, Claude Skills/Plug\-ins, and agentic workflows
- Balance trade\-offs across scope, speed, and quality to protect delivery timelines and outcomes
- Translate ambiguous business requests into scoped, governed, production\-ready solutions with a clear owner and success metric
- Codify successful patterns into reusable playbooks, templates, and frameworks that scale beyond a single deployment
- Provide structured feedback on real\-world usage and performance data to IT and the AI Advisory Council
- Identify and flag data governance risks, including access control gaps and write\-back conflicts, before they become production issues
- Develop an operating model to scale Playbooks and agents across Business Transformation Managers and Operations teams (e.g., workflow ownership, change management, adoption, etc.)
What You Will Need
- Proven experience deploying AI/ML systems into production, including LLM\-based applications, with a strong understanding of how model behavior affects real business workflows
- Experience working directly with senior stakeholders, able to translate technical concepts into business value and drive adoption
- Demonstrated ability to scope and deliver complex systems in fast\-moving, ambiguous environments, with strong judgment on risk and prioritization
- A combination of engineering, consulting, and product thinking, with strong systems and architectural judgment and an ownership mentality
- Excellent communication skills across technical and non\-technical audiences, with the ability to simplify complexity and drive alignment
- Comfort operating inside a structured governance model, balancing speed with the discipline required at enterprise scale
What you will gain:
At Intapp, you'll get the opportunity to bring your talents and intellectual curiosity to create better outcomes for our people and our clients. You'll be part of a growing public company, with a modern work environment that's connected yet flexible and where your professional growth and well\-being are top priorities. We'll collaborate and grow together, supporting each other in a positive, open atmosphere that fosters creativity, innovation, and teamwork.
Here, you will have the opportunity to:
- Expand Your Skills: Unlock your potential with professional development opportunities supported by a community of experienced professionals. We offer reimbursement for training and continuing education to help you stay ahead in your career.
- Enjoy Where You Work: Thrive in our modern, open offices designed to inspire creativity and collaboration. Our complimentary lunches and fully stocked kitchens ensure you have everything you need to stay energized throughout the day.
- Support What Matters Most: Our comprehensive wellness and flexible time off programs and our benefits are designed to care for you and your family. Our family\-formation benefits and support during your family\-building journey ensure you have the resources you need when it matters most. We believe in giving back and supporting our communities with paid volunteer time off and a donation matching program for the causes you care about.
Join us and be a part of a collaborative and welcoming culture where your contributions are valued, and your professional growth is a priority. Together, we are building a company of long\-term value that we can all be proud of.
*Intapp provides equal employment opportunities to all qualified applicants and will make hiring decisions without regard to race, color, sex, sexual orientation, gender identity or expression, religion, national origin or ancestry, age, disability, marital status, pregnancy, protected veteran status, protected genetic information, political affiliation, or any other characteristic protected by federal, state or local laws.*
*Please note: Intapp will not hire through text message, social media, or email alone. We will never extend a job offer unless you have been contacted directly by an Intapp recruiter and have participated in the interview process which will generally consist of 3 or more virtual or in person meetings. Please note that Intapp only uses company email addresses, which contain “@intapp.com” or “@dealcloud.com” to communicate with candidates via email. Intapp will never ask for financial information of any kind or for any payment during the job application process. We post all legitimate job openings on the Intapp Career Site at* *https://www.intapp.com/working\-at\-intapp/**.* *If you believe you were a victim of such a scam, you may contact your local authorities. Intapp is not responsible for any claims, losses, damages, or expenses resulting from scammers.*
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 Intapp, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Intapp AI Hiring
Intapp has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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