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About This Role
Description
Executive Assistant and Team Coordinator, Chief AI \& Knowledge Officer
About Alvarez \& Marsal
Alvarez \& Marsal (A\&M) is a global consulting firm with entrepreneurial, action and results\-oriented professionals. We take a hands\-on approach to solving our clients' problems and assisting them in reaching their potential. Our culture celebrates independent thinkers and doers who positively impact our clients and shape our industry. The collaborative environment and engaging work—guided by A\&M's core values of Integrity, Quality, Objectivity, Fun, Personal Reward, and Inclusive Diversity—are why our people love working at A\&M.
The Team
The Global AI \& Knowledge Organization (GAIKO) is A\&M's central engine for AI strategy, enablement, and knowledge infrastructure. Led by the Chief AI \& Knowledge Officer, the team bridges firm strategy and frontline delivery, responsible for how A\&M adopts, governs, and scales AI across its practices and geographies.
The team's mandate spans the full lifecycle of AI at A\&M: from rolling out enterprise tools like Claude to thousands of practitioners across the US, Australia, and Europe, to building the platforms and governance frameworks that ensure AI work is reusable, compliant, and compounding in value over time.
How you will contribute
The Executive Assistant \& Team Coordinator will be an essential operational partner to GAIKO's senior leadership — providing classic EA support to the Chief AI \& Knowledge Officer and key team leaders, while also owning the logistics and coordination infrastructure that keeps the team running smoothly.
This is a high\-trust, high\-visibility role sitting at the center of one of A\&M's most strategically important teams. The right candidate will bring prior EA experience within A\&M or a comparable professional services environment, a proactive mindset, and the organizational polish to manage competing priorities across a fast\-moving global team.
Executive Support
- Manage and coordinate calendars for the Chief AI \& Knowledge Officer and designated senior leaders, including scheduling internal and external meetings across time zones
- Handle end\-to\-end travel arrangements including flights, hotels, and ground transportation
- Prepare and submit expense reports on behalf of senior leaders using Concur, ensuring timely and accurate reconciliation
- Draft, proofread, and format correspondence, presentations, and briefing materials as needed
- Act as a trusted gatekeeper, managing information flow, flagging priorities, and ensuring follow\-through on commitments
- Build and maintain effective relationships with leadership, support staff, and external contacts to facilitate smooth scheduling, communications, and coordination
- Exercise sound judgment in managing competing priorities, knowing when to act independently and when to escalate
- Serve as a reliable backup and collaborative partner to other assistant team members across the firm as needed
Team Coordination
- Own the planning and logistics for recurring team meetings, room or virtual setup, materials distribution, and follow\-up on action items
- Coordinate logistics for cross\-functional AI governance meetings, including the AI Operating Committee (AI OC), ensuring attendees, materials, and follow\-ups are managed end\-to\-end
- Maintain team calendars, distribution lists, and shared resources to keep the broader GAIKO team organized and aligned
- Support onboarding logistics for new team members, including equipment, access, and orientation scheduling
Events \& Special Projects
- Plan and coordinate internal team events, offsites, and working sessions — managing venue, catering, travel, and communications
- Support 1–3 external events per year possibly requiring travel and on\-site coordination
- Assist with department and operational projects as needed, including preparation of materials for leadership reviews and firm\-wide communications
Qualifications
Required
- 5 – 10\+ years prior experience in an EA, team coordinator, or administrative operations role within a consulting or professional services environment
- Bachelor's degree preferred, or equivalent experience
- Highly proficient in Microsoft Office Suite (Outlook, Word, PowerPoint, Excel, SharePoint), Concur, Claude
- Strong interpersonal and relationship\-building skills, with the ability to support multiple leaders with different working styles
- Excellent written and verbal communication skills — clear, professional, and concise
- Demonstrated ability to manage competing priorities independently in a fast\-paced, high\-expectation environment
- High attention to detail, discretion, and follow\-through
Preferred
- Prior experience supporting a Chief\-level executive or senior leadership team
- Familiarity with a range of AI tools or knowledge management platforms is a plus
- Experience coordinating large cross\-functional meetings or governance forums
Your journey at A\&M
We recognize that our people are the driving force behind our success, which is why we prioritize an employee experience that fosters each person’s unique professional and personal development. Our robust performance development process promotes continuous learning, rewards your contributions, and fosters a culture of meritocracy. With top\-notch training and on\-the\-job learning opportunities, you can acquire new skills and advance your career.
We prioritize your well\-being, providing benefits and resources to support you on your personal journey. Our people consistently highlight the growth opportunities, our unique, entrepreneurial culture, and the fun we have together as their favorite aspects of working at A\&M. The possibilities are endless for high\-performing and passionate professionals.
Regular employees working 30 or more hours per week are also entitled to participate in Alvarez \& Marsal Holdings’ fringe benefits consisting of healthcare plans, flexible spending and savings accounts, life, AD\&D, and disability coverages at rates determined periodically as well as a 401(k) retirement savings plan. Provided the eligibility requirements are met, employees will also receive an annual discretionary contribution to their 401(k) retirement savings plan from Alvarez \& Marsal. Additionally, employees are eligible for paid time off including vacation, personal days, seventy\-two (72\) hours of sick time (prorated for part time employees), ten federal holidays, one floating holiday, and parental leave. The amount of vacation and personal days available varies based on tenure and role type. Click here for more information regarding A\&M’s benefits programs.
The salary range is $80,000 \- $95,000 annually, dependent on several variables including but not limited to education, experience, skills, and geography. In addition, A\&M offers a discretionary bonus program which is based on a number of factors, including individual and firm performance. Please ask your recruiter for details.
Salary Context
This $80K-$95K range is in the lower quartile 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 Alvarez & Marsal, 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. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($87K) sits 59% below the category median. Disclosed range: $80K to $95K.
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.
Alvarez & Marsal AI Hiring
Alvarez & Marsal has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Atlanta, GA, US. Compensation range: $95K - $230K.
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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