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About This Role
Description
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
You will be joining a newly formed Global AI & Knowledge Innovation Office (GAIKO) led by a Chief AI & Knowledge Officer who is moving fast. The team is global, still being staffed, and operating with urgency. This role sits alongside pillars covering Responsible AI & Governance, AI Portfolio Intelligence & Value, and Governed Innovation, as well as a separate product and technology organization.
How you will contribute
A&M's AI ambition depends on a growing set of strategic vendor and partner relationships — the AI model providers, platforms, and specialist firms the firm builds on and sells alongside. Right now, those relationships are managed informally and unevenly: some are deep and senior, others exist only at the account-management level, and there is no single place to see partner status, tier, or approval health across the portfolio.
This role owns that relationship layer end to end: the strategy for which partners matter and why, the senior-level engagement that makes A&M a partner of choice rather than just another logo, the operational tracking that keeps approvals and renewals from lapsing silently, and the coordination that gets priority partnerships through review faster. This is a relationship and coordination role, not a procurement, security, or product-build role. Those functions continue to own their own processes; this role removes friction and builds the case for investment.
Key Responsibilities
Partnership Strategy
- Define the firm's overall AI partnership strategy: which categories of partner the firm needs (model providers, platforms, specialist firms), how many relationships to hold in each category, and what \good\ looks like at each tier
- Set the criteria for how partners are evaluated, tiered, and prioritized, and keep that framework current as the AI vendor landscape shifts
- Translate business unit and firm-wide growth priorities into a point of view on where new partnerships are worth pursuing versus where existing ones should be deepened, consolidated, or exited
- Present the strategy and its rationale to firm leadership, and own it as a living view that gets revisited as the market and the firm's needs change
Partner Relationship Management
- Enable and coordinate A&M's strategic AI partner relationships on behalf of the practitioners and BU leaders who own them day-to-day.
- Track and report on the health, scope, and terms of each partnership
- Own the go/no-go recommendation and business case for tier changes or new partner relationships
Senior Stakeholder Engagement & Narrative Building
- Build and maintain relationships with senior leaders — not just day-to-day account teams — at each key partner firm, so A&M is seen as a visible, credible, senior-level partner of choice
- Develop the narrative and supporting materials — pitch decks, one-pagers, case studies, practitioner and client success stories — that make the case for why a partner should invest more deeply in A&M, and vice versa
- Represent A&M at partner-level executive briefings, quarterly business reviews, and strategic planning sessions
Partner–BU Matching & Opportunity Surfacing
- Proactively surface partnership opportunities in both directions: from business unit demand for a capability a partner offers, and from independent market scanning of capabilities BUs don't yet know exist
- Match the right partner to the right business unit use case, and broker the initial connection
Partner Inventory & Relationship Intelligence
- Create and maintain a living inventory of all strategic AI partners: relationship owner, tier/status, strength of relationship, key contacts, scope of the relationship, and vendor approval/expiration dates
- Be the single source of truth on whether the firm is current with each vendor, so approvals don't lapse silently
- Establish a regular cadence and format for communicating the state of these relationships across the firm — who our partners are, what each relationship covers, how to engage them, and what's changed — so business units and leadership aren't relying on word of mouth
- Make the inventory and relationship updates easily discoverable (e.g., a firm-wide partner directory or briefing) so any practitioner can quickly find the right partner and the right contact for a given need
Fast-Lane Facilitation
- Partner directly with the firm's procurement and security/risk functions to expedite review and approval for priority partners and time-sensitive opportunities
- Own the coordination, not the underlying review — the goal is removing friction and cutting approval timelines for strategic priorities, not replacing procurement's or security's judgment
Capability Activation (incl. Claude Partner Network Rollout)
- Drive the certification pipeline for key partnerships: identify the right people for partner courses and certifications, secure exam budgets, and track completion
- Capture and package the resulting practitioner and client stories as proof points for the partnership's value
- Leverage existing enablement and adoption channels rather than building new ones
Scope Boundaries
- Vendor security review or procurement intake itself sits with Procurement and Security/Risk
- License/seat provisioning and administration sits with the Product team, in partnership with Procurement
- Formal product build or scale decisions sit with the Product team and the business units
Qualifications
Required
- 7+ years in partnerships, alliances, business development, or ecosystem management, ideally with a technology or AI vendor ecosystem
- Comfortable operating at senior/executive level, both internally and with partner organizations
- Strong project-coordination instincts — able to work across procurement, security, and business unit stakeholders without owning their processes
- Strong written and verbal communication skills, with demonstrated ability to build compelling narrative and briefing materials
Preferred
- Experience building narrative or marketing materials (case studies, executive briefing decks)
- Familiarity with the AI vendor landscape (model providers, data/analytics platforms, or similar)
- Prior experience in a consulting or professional services firm
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 $150,000-$165,000 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.
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Salary Context
This $150K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($157K) sits 26% below the category median. Disclosed range: $150K to $165K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Alvarez & Marsal AI Hiring
Alvarez & Marsal has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Central & Western District, Hong Kong SAR, New York, NY. Compensation range: $165K - $165K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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