Interested in this AI/ML Engineer role at Prosci?
Apply Now →About This Role
The Principal, AI Transformation Lead is the person a client's leadership team trusts with the question they have not figured out yet: what can AI actually do to transform our business, and how do we make that happen? Building that client trust is the job, and everything else follows from it. The Principal will own a book of AI transformation business from the first conversation to the delivered result. This is a sell-and-deliver role: the person who wins the work also leads it, which means credibility comes from being in the work rather than from just building a deck about it. The Principal will carry a bookings target, run the sale, then lead the engagement — showing a client where AI changes their work, running workshops with their teams to build AI capabilities, proving value with working prototypes, and guiding their people through the change.
Finding the work comes first. The Principal will build their own pipeline, and will work with Prosci's existing sales teams to find AI transformation opportunity inside the client relationships Prosci already holds after three decades of enterprise work. The practice delivers AI transformation as prime and is accountable for the outcome; a network of certified delivery partners carries out larger-scale customer software builds. The team is small by design, so the Principal will direct more work than they personally perform, and is expected to be AI-native in approach.
Since 1994, Prosci has focused on change. We pair a deep understanding of people with a proven methodology rooted in the ADKAR® model, and our training, advisory services, and licensing options (including Kaiya, our AI change management coach) help organizations build lasting change resiliency. We are a purpose-driven team of curious, results-oriented people working toward a world where change is done right.
Check out our website for more about our team and approach: https://www.prosci.com/about*.*
Key role responsibilities include but are not limited to the following:
Business Development* Carry a bookings target and own the book of business the role creates. The Principal is accountable for what gets sold, not only what gets delivered.
- Become the person a CHRO, COO or transformation lead calls early, before the plan is set, and protect that relationship, including when the right advice is not in Prosci's short-term interest.
- Run the full sale from first meeting to close: qualification, scoping, pricing and proposal.
- Build pipeline through speaking, thought leadership and outreach to a named target account list.
- Work with Prosci's sales teams to find AI transformation opportunities inside accounts Prosci already holds and open those conversations together under the established rules of engagement.
- Turn certification, Membership and practitioner relationships into organizational conversations, and pass core change management work to the right seller.
Client Delivery* Learn how the client makes money, where the friction sits, and how change has landed with their people before, and frame the work in their language rather than Prosci's.
- Lead the engagements the role sells, as the senior Prosci voice and the client's point of accountability.
- Facilitate AI integration working sessions and executive briefings, bringing in subcontracted facilitators as accounts grow.
- Map how a team works, show where AI changes that work rather than adds to it, and build a first working prototype in their own environment.
- Lead the people side of the change, bringing forward Prosci's 30 years of discipline and research on how successful change happens, applying the Prosci Methodology and the ADKAR® Model.
Account Growth* Grow each account one team at a time: win with the first team, turn that into sponsorship, then open the next function.
- Convert delivered work into recurring coaching and expert-access revenue and scope the larger work redesign engagements that follow.
- Carry six to ten accounts at once without loss of quality and own the commercial health of the book.
Partners and Technical Scoping* Direct technical delivery through certified delivery partners so the client deals with one accountable partner, held to scope, timeline and quality.
- Stay fluent enough to build a first-draft agent and judge vendor claims, and route production-grade work to partners early.
Metrics & Outcomes* Accountable for the role's primary KPIs: originated bookings, opportunity sourced from existing accounts, bookings attainment, teams per account, recurring revenue, delivery margin and client NPS.
- Land new accounts with a first paid team engagement, grow one to a second function in year one, and document the motion well enough to train the next Principal against it.
Requirements
Based on this role’s scope and responsibilities, we are seeking candidates with the following minimum qualifications, skills, attributes, and competencies. Preferred qualifications are also noted.
Skills* Consultative selling of six and seven-figure professional services to executive buyers, including pricing, proposals and deal structuring.
- Building pipeline through speaking, outreach, and growing existing accounts alongside an established sales team.
- Facilitating executive and team working sessions, and leading the people side of change.
- Practical use of enterprise AI assistants and agent tools, enough to build a first draft and scope a build.
Qualifications* 7+ years in consulting, professional services or agency work where senior people both sell and deliver.
- A record of winning and closing consulting or advisory work against a number, including selling into an existing customer base alongside an established sales team.
- Experience leading AI, digital or business transformation work in complex enterprise environments.
- Hands-on builder with current AI models and agent tools, with prototypes or workflows you can walk us through
- Change management or transformation background strongly preferred; Prosci certification valued, not required at hire.
Competencies* Drives Results: Delivers the number and does not let delivery work become the reason it slips.
- Persuades: Wins pursuits where the buyer is still forming the problem and secures sponsorship for work that changes jobs.
- Customer Focus: Stays close enough to the work to see the next opportunity before the client asks.
- Business Insight: Understands rate, margin and cost to acquire, and structures work that holds up in a P&L review.
- Tech Savvy: AI-native in how they work, fluent with frontier AI tools, and able to build a working first draft and scope technical work credibly.
- Manages Ambiguity: Sells and delivers while the offering and pricing are still moving.
- Manages Complexity: Holds six to ten accounts and partner workstreams at once.
- Builds Networks: Turns speaking, thought leadership and Prosci's existing relationships into originated pipeline.
- Collaborates: Co-sells with core sellers without competing for credit.
- Communicates Effectively: Explains a complex offering to executives who are not AI or change management experts.
Benefits Additional Information
Travel Requirements: This role may require up to 20% travel for client meetings and internal team meetings.
Work Location: Remote within the US.
Compensation: $154,500 - $193,000 is the projected range of annual base salary for this role depending on the candidate’s overall qualifications and experience. This role is eligible for inclusion in our bonus & commission programs.
Benefits: Through our experience and strategic focus on the people side of change, we know that by taking care of our people, we are taking care of our business. In addition to the compensation detailed above, we offer comprehensive wellness benefits, including generous flexible paid time off, holidays and volunteer time, medical, dental, vision, long-term and short-term disability programs, life insurance, pet insurance, 401k with company matching, and access to LinkedIn Learning. Our “mostly virtual” culture is vibrant with many opportunities to collaborate with colleagues from around the world, get involved in employee-led interest and resource groups, and to meet up with team members at in-person local or market-wide events.
We hope you're interested in making a difference with us. Even if you don't feel that you meet every requirement listed in this job description, we still encourage you to apply. Prosci is an equal opportunity employer that is committed to inclusion and diversity. Employment opportunities at Prosci are available to all applicants, without regard to race, color, religion, sex, sexual orientation, gender identity, genetic information, national origin, age, disability, veteran status, or any other legally protected characteristics. You can learn more about our efforts to build a more inclusive, equitable and diverse organization:
https://www.prosci.com/about/dei
If you require assistance due to a disability applying for open positions, please submit a request to: [email protected].
Salary Context
This $154K-$193K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Prosci, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($173K) sits 18% below the category median. Disclosed range: $154K to $193K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Prosci AI Hiring
Prosci has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $193K - $193K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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