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About This Role
### Job Description
As a Principal within Kearney's Digital \& Analytics (D\&A) practice, you will be a senior leader in our Digital Operations team, specializing in Agentic Procurement Transformation.
In this role, you will help global organizations reimagine and optimize their procurement functions using data science, advanced analytics, and best\-in\-class procurement platforms. You will lead complex, multi\-workstream engagements for clients across industries, drive measurable cost and efficiency value, and play a central role in scaling Kearney's Agentic procurement capabilities \- through business development, thought leadership, and talent development.
This is an exciting opportunity to shape one of Kearney's fastest\-growing capabilities at the intersection of strategy, procurement, data science, and digital transformation. Key Responsibilities* Lead and deliver large\-scale agentic procurement transformation programs using tools like Coupa, SAP Ariba, Jaggaer, GEP SMART, Ivalua, Zip, Oro, and Levelpath to design efficient, resilient, and cost\-optimized source\-to\-pay processes
- Own client relationships at the executive level \- serving as a trusted advisor, ensuring delivery excellence across engagements, and identifying opportunities to expand impact
- Translate executive priorities into analytical models and digital strategies that drive decision\-making across sourcing, category management, contract lifecycle management, and supplier risk functions
- Leverage large spend datasets, enterprise systems (e.g., SAP, Oracle), procurement platforms, and cloud analytics solutions to enable and accelerate agentic procurement transformation
- Drive business development by originating new client opportunities, leading proposal development, and actively participating in pitches across existing and prospective accounts
- Develop and commercialize thought leadership on emerging trends including autonomous procurement, AI\-powered spend analytics, supplier risk management, ESG\-driven sourcing, and should\-cost modeling
- Build Kearney's agentic procurement IP \- developing reusable assets, proprietary frameworks, and new offerings that differentiate our capabilities in the market
- Coach and mentor consulting managers and junior colleagues, investing in the growth of Kearney's agentic procurement talent and team culture
- Collaborate across D\&A and adjacent practices—including Operations, Strategy, and M\&A—to bring integrated, high\-impact solutions to clients' most complex procurement challenges
Who You Are
After nearly 100 years, we know this business is fundamentally about making connections—between facts, figures, insights, strategies, tools, technologies, and above all, people. That's why we look for collaborative, insightful, and inquisitive problem\-solvers who don't accept the first answer in front of them and who are always unapologetically themselves.
We want to hear from you if you are:* Experienced: 10\+ years in management consulting or procurement analytics, ideally within a top\-tier firm or digitally\-focused organization, with a demonstrated track record at the Principal level
- Functionally grounded: Deep expertise in end\-to\-end procurement processes—category strategy, strategic sourcing, supplier management, S2P/P2P, and contract lifecycle management—with experience leading transformation programs across industries such as consumer goods, healthcare, retail, and industrials
- Technically strong: Hands\-on familiarity with the procurement platform landscape (e.g., Coupa, SAP Ariba, Jaggaer, GEP SMART, Ivalua, Zip, Oro, Levelpath) and analytics tools (e.g., Tableau, Power BI), with the ability to guide technology strategy and selection at the enterprise level
- Digitally fluent: Able to integrate procurement data, platforms, and analytics into large\-scale operational transformations and articulate a clear point of view on the evolving agentic procurement landscape
- A proven business developer: Experienced in managing proposal processes, leading client pitches, and growing accounts—with the commercial instincts to identify and pursue new opportunities
- A leader and collaborator: Energized by managing high\-performing cross\-functional teams and building trusted, long\-term relationships with senior client stakeholders
- A structured thinker: Able to navigate ambiguity, decompose complex procurement and supplier challenges, and deliver clear, actionable solutions with executive\-level communication
- Academically accomplished: Master’s degree in Supply Chain Management, Industrial Engineering, Business Analytics, Finance, or a related field; MBA preferred
- Entrepreneurial: Eager to build, scale, and influence a high\-growth capability within a global firm—and to leave a mark on how Kearney serves the market
- Located near or willing to relocate to Atlanta, Boston, Chicago, Dallas, Southfield, New York, San Francisco, Toronto, or Washington D.C.
What we can offer you:
Every day, our people work to be the difference for our clients, our communities, and our colleagues. Helping them make an impact, they are sustained by a competitive remuneration package plus comprehensive benefits and perks, including but not limited to:
- Generous retirement/pension savings contributions
- Comprehensive medical insurance for employees and immediate family
- Gym membership discounts
- Non\-partner equity\-based awards (for consulting managers and above)
- Structured and on\-the\-job learning and development opportunities
- Personalized opportunities including talent mobility, flexible work programs, and externships to help you chart a unique career journey to pursue your own personal and professional goals
*
Principal base compensation – $245,000\-$270,000\. *It is important to note that at Kearney, it is not typical for an individual to be hired at the top of the range for their role. Individual salaries within each range are determined through a wide variety of factors including but not limited to education, experience, knowledge and skills. Kearney reviews compensation regularly and may adjust base salaries to reflect market competitiveness. In addition to salary, individuals may be eligible for a discretionary performance bonus. Our full suite of benefits includes paid time off, 401(k) match and profit sharing, medical, dental and vision coverage, healthcare concierge, backup child/adult care, annual employer HSA contribution, home office stipend, subsidized Gympass and annual wellness program, and leaves of absence when needed to support employees’ physical, mental, and emotional well\-being.*
Read more about our benefits and a career at Kearney.com/careers. Equal Employment Opportunity and Non\-Discrimination
Kearney prides itself on providing a culture that allows employees to bring their best selves to work every day. Our people can feel comfortable, confident, and joyful to do great things for our firm, our colleagues, and our clients. Kearney aims to build diverse capabilities to help our clients solve their most mission critical problems.
Kearney is committed to building a diverse, unbiased and inclusive workforce. Kearney is an equal opportunity employer; we recruit, hire, train, promote, develop, and provide other conditions of employment without regard to a person's gender identity or expression, sexual orientation, race, religion, age, national origin, disability, marital status, pregnancy status, veteran status, genetic information or any other differences consistent with applicable laws. This includes providing reasonable accommodation for disabilities, or religious beliefs and practices. Members of communities historically underrepresented in consulting are encouraged to apply. \#LI\-DNP
Salary Context
This $245K-$270K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Kearney, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($257K) sits 18% above the category median. Disclosed range: $245K to $270K.
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.
Kearney AI Hiring
Kearney has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $194K - $270K.
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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