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About This Role
Role Overview
Gartner Consulting is seeking a Engagement Manager with deep fluency in artificial intelligence to lead high\-impact client engagements at the intersection of strategy, technology, and enterprise transformation. This is a client\-facing leadership role responsible for shaping AI strategy, guiding delivery teams, and translating Gartner's research and proprietary methodologies into measurable business outcomes for large enterprise and public\-sector clients. The ideal candidate for this role has had hands\-on experience successfully deploying AI in enterprise environments and can advise clients on the critical success factors and common pitfalls of complex AI deployments. The Director operates as a trusted advisor to C\-suite stakeholders (CIO, CDAO, CTO, CEO) and a driving force behind the growth of Gartner's AI Center of Excellence.
Key Responsibilities:
Client Delivery \& Advisory
- Lead complex AI engagements as engagement manager end\-to\-end—from opportunity scoping and diligence through solution design, roadmap development, and implementation oversight—ensuring rigor, quality, and on\-time delivery.
- Serve as the primary executive point of contact for clients, building durable relationships with senior stakeholders and acting as a credible authority on enterprise AI adoption.
- Translate ambiguous business problems into structured, decision\-ready recommendations , distinguishing genuine signal from hype in a rapidly evolving AI landscape.
- Guide clients through technical AI enablement strategy, operating\-model design, workflow transformation and use\-case prioritization, data\-readiness evaluation, architecture, and responsible\-AI governance .
- Pull from prior experience to advise clients with respect to opportunities and risks/constraints/trade\-offs in deploying AI at enterprise scale
AI Domain Leadership
- Maintain hands\-on fluency across the modern AI stack: generative AI and LLMs, agentic architectures, RAG, machine learning, MLOps/LLMOps, data platforms, and the leading hyperscaler and vendor ecosystems (Microsoft/Azure OpenAI, Google, AWS, OpenAI, Anthropic, etc.).
- Provide technical depth of one or more AI\-enabled platforms (e.g., Copilot Studio, ServiceNow, Agentforce, Bedrock, Sana, Azure AI Foundry, Google Vertex AI, Gemini Enterprise, OpenAI Enterprise, Claude for Enterprise, Microsoft 365 Copilot, Glean, Moveworks, Writer, Dataiku, DataRobot, Palantir AIP, UiPath, IBM watsonx, Databricks Mosaic AI, Snowflake Cortex) and integration with other enterprise systems
- Advise on AI strategy and value realization —linking technology choices to ROI, TCO, risk posture, and change\-management implications.
- Shape AI governance and responsible\-AI frameworks , including model risk, data privacy, security, regulatory alignment.
- Stay ahead of market movements, feeding emerging trends back into Gartner research, IP, and client conversations.
Business Development \& Growth
- Support business development \- driving pipeline generation, proposal development, scoping, and closing of AI\-focused consulting engagements.
- Partner with Gartner Consulting partners and account teams to identify and convert opportunities , expanding footprint within existing accounts and opening new logos.
- Contribute to thought leadership —points of view, workshops, symposium sessions, and reusable accelerators that differentiate Gartner in the market.
Team \& Capability Building
- Lead, mentor, and develop multidisciplinary consulting teams, setting delivery standards and growing internal AI capability.
- Contribute to the AI Center of Excellence , building case studies, offerings, and repeatable methodologies that scale across regions and industries.
Required Qualifications
- 10\+ years of experience in management consulting, technology strategy, or enterprise transformation, with a substantial and recent focus on AI/data .
- Demonstrated ability to lead C\-level conversations and sell/deliver multimillion\-dollar engagements.
- Depth of experience in one or more industries (i.e., manufacturing, financial services, healthcare, high\-tech, professional services, public sector).
- Strong technical literacy: able to engage credibly with IT leaders and engineers while translating for business executives.
- Proven track record in AI strategy, data strategy, operating\-model design, or large\-scale AI/ML implementation .
- Bachelor's degree required; advanced degree (MBA, MS in CS/Data Science, or equivalent) strongly preferred.
- Exceptional structured\-thinking, executive\-communication, and storyline/deck\-building skills.
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Who are we?
At Gartner, Inc. (NYSE:IT), we guide the leaders who shape the world.
Our mission relies on expert analysis and bold ideas to deliver actionable, objective business and technology insights, helping enterprise leaders and their teams succeed with their mission\-critical priorities.
Since our founding in 1979, we’ve grown to 20,000 associates globally who support over 13,000 client enterprises in \~90 countries and territories. We do important, interesting and substantive work that matters. That’s why we hire associates with the intellectual curiosity, energy and drive to want to make a difference. The bar is unapologetically high. So is the impact you can have here.
What makes Gartner a great place to work?
Our vast, virtually untapped market potential offers limitless opportunities – opportunities that may not even exist right now – for you to grow professionally and flourish personally. How far you go is driven by your passion and performance.
We hire remarkable people who collaborate and win as a team. Together, our singular, unifying goal is to deliver results for our clients.
Our teams are inclusive and composed of individuals from different geographies, cultures, religions, ethnicities, races, genders, sexual orientations, abilities and generations.
We invest in great leaders who bring out the best in you and the company, enabling us to multiply our impact and results. This is why, year after year, we are recognized worldwide as a great place to work.
Gartner is the world authority on AI
At Gartner, you’ll join a company at the very center of the AI revolution. Gartner has proactive, objective guidance throughout clients’ AI journeys. We set the standard for how organizations leverage artificial intelligence to drive meaningful impact. You’ll have access to unmatched resources, expertise, and technology, and play a key role in helping Gartner and our clients innovate and grow as we leverage AI to transform business and technology landscapes.
It’s an exciting time to be at Gartner, with limitless opportunities to make a real impact, grow your skills, and build a lasting, meaningful career in a field that’s reshaping the way we operate. If you’re passionate about AI and want to be part of a team that’s guiding the leaders who shape the world, Gartner is the place for you.
What do we offer?
Gartner offers world\-class benefits, highly competitive compensation and disproportionate rewards for top performers.
In our hybrid work environment, we provide the flexibility and support for you to thrive — working virtually when it's productive to do so and getting together with colleagues in a vibrant community that is purposeful, engaging and inspiring.
Ready to grow your career with Gartner? Join us.
Gartner believes in fair and equitable pay. A reasonable estimate of the base salary range for this role is 168,000 USD \- 199,000 USD. Please note that actual salaries may vary within the range, or be above or below the range, based on factors including, but not limited to, education, training, experience, professional achievement, business need, and location. In addition to base salary, employees will participate in either an annual bonus plan based on company and individual performance, or a role\-based, uncapped sales incentive plan. Our talent acquisition team will provide the specific opportunity on our bonus or incentive programs to eligible candidates. We also offer market leading benefit programs including generous PTO, a 401k match up to $7,200 per year, the opportunity to purchase company stock at a discount, and more.
The policy of Gartner is to provide equal employment opportunities to all applicants and employees without regard to race, color, creed, religion, sex, sexual orientation, gender identity, marital status, citizenship status, age, national origin, ancestry, disability, veteran status, or any other legally protected status and to seek to advance the principles of equal employment opportunity.
Gartner is committed to being an Equal Opportunity Employer and offers opportunities to all job seekers, including job seekers with disabilities. If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to use or access the Company’s career webpage as a result of your disability. You may request reasonable accommodations by calling Human Resources at \+1 (203\) 964\-0096 or by sending an email to [email protected] .
Job Requisition ID:113000
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Salary Context
This $168K-$199K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Gartner, 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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($183K) sits 15% below the category median. Disclosed range: $168K to $199K.
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.
Gartner AI Hiring
Gartner has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Chicago, IL, US, TX, US, Remote, US. Compensation range: $133K - $202K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% 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 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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