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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
The AI Engineering Lead and Architect is the Product Owner's principal technical counterpart for A&M's Intelligence Layer. This person owns the technical architecture, helps the Product Owner evaluate proposed directions, and translates the Product Owner's decisions into clear engineering direction for internal teams and delivery partners. The role combines architecture, engineering leadership, and hands-on development. It requires someone who can direct engineers, create clear architecture diagrams, review technical work, and write production code when the team needs it. This is a senior role on a small team with ambitious timelines and significant visibility. This is an opportunity to shape a state-of-the-art AI architecture designed to serve an entire enterprise. Success requires forward-looking technical judgment, curiosity about emerging approaches, and the ability to turn innovative ideas into secure production systems on an ambitious timeline.
The Lead and Architect will work alongside the implementation partner throughout the initial production build, jointly shape and validate the architecture, contribute directly to critical engineering work, and develop deep working knowledge of the complete platform. This person will establish internal technical ownership from the outset and assume primary responsibility for future iterations as the partner engagement concludes.
How you will contribute
- Own the end-to-end technical architecture and engineering roadmap for the Intelligence Layer, including knowledge graphs, ingestion, retrieval, APIs, user-facing capabilities, security, and integration with A&M's Data Layer and enterprise systems.
- Serve as the Product Owner's technical counterpart by testing proposed approaches, explaining tradeoffs, recommending a course of action, and translating final product decisions into executable technical direction.
- Direct internal engineers and delivery partners, set priorities and technical standards, review designs and code, resolve blockers, and hold delivery teams accountable for scope, quality, and milestones.
- Create and maintain architecture diagrams, data-flow diagrams, integration specifications, technical decision records, and implementation guidance for technical and non-technical audiences.
- Design knowledge graph schemas, entity models, ingestion pipelines, and retrieval patterns using Neo4j and Cypher or comparable graph technologies.
- Write, review, and ship production code, primarily in Python and API development, and contribute to front-end implementation when needed.
- Architect the integration of structured data and unstructured content from multiple business units and enterprise systems while preserving permissions, ethical walls, and governance requirements.
- Design and guide MCP servers, agent integrations, authentication, authorization, and enterprise identity patterns for approved large language model platforms.
- Architect and lead the development of production agentic systems and pipelines, including multi-step orchestration, planning, tool routing, MCP integrations, retrieval, state and memory, evaluation, guardrails, observability, and human-in-the-loop controls.
- Ensure the platform meets requirements for client confidentiality, case isolation, security, reliability, observability, maintainability, and cost management.
- Communicate delivery status, technical risks, dependencies, and decisions clearly to the Product Owner, firm leadership, and partner teams.
First Year Outcomes
- Deliver the initial production iteration of the Intelligence Layer knowledge graphs and the technical foundation needed to extend them.
- Establish the enterprise agentic architecture and reusable pipeline patterns needed to support secure, governed agent workflows across business units and use cases.
- Enable secure global search and business unit specific search across approved structured and unstructured data sources.
- Establish a coherent reference architecture, implementation roadmap, and set of reusable engineering patterns for future business units and capabilities.
- Build an effective engineering operating model in which internal teams and delivery partners work from clear standards, decisions, diagrams, and acceptance criteria.
- Give the Product Owner reliable technical validation and actionable options for major architecture, scope, security, and delivery decisions.
Qualifications
Required Qualifications
- Eight or more years of software engineering experience, including technical leadership or architecture responsibility for complex platforms.
- Demonstrated experience directing internal engineering teams and third-party delivery vendors, including setting technical direction, reviewing their work, resolving delivery issues, and holding teams accountable while remaining a strong hands-on individual contributor.
- Production experience designing and building knowledge graphs, with strong Neo4j and Cypher experience preferred.
- Full-stack fluency across modern front-end frameworks, back-end services, APIs, data pipelines, and strong Python development.
- Experience creating architecture diagrams and technical specifications that engineering teams can implement.
- Substantial experience with Claude, ChatGPT, or comparable large language model platforms, including hands-on experience building MCP servers, AI agent tools, or comparable enterprise AI integrations.
- Demonstrated experience designing and shipping production agentic systems, including orchestration, tool use, state management, evaluation, guardrails, observability, and human oversight.
- Hands-on experience designing and implementing enterprise authentication and authorization using Microsoft Entra ID, OAuth 2.0, OpenID Connect, token scoping, on-behalf-of flows, and secrets management.
- Experience integrating structured and unstructured enterprise data with appropriate security, privacy, and governance controls.
- Ability to evaluate competing technical approaches, explain tradeoffs clearly, and convert product decisions into delivery plans.
- Strong communication skills across engineering, product, security, business, and executive audiences.
Preferred Qualifications
- Deep experience with Microsoft Azure services for compute, data, search, identity, and AI.
- Experience with Snowflake and enterprise data platforms.·
- Experience in professional services, consulting, or another environment that handles client-sensitive information.
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 $220,000 - $250,000 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 $220K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($235K) sits 11% above the category median. Disclosed range: $220K to $250K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Alvarez & Marsal AI Hiring
Alvarez & Marsal has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Madrid, Community of Madrid, Spain, Gurugram, Haryana, India, New York, NY, US. Compensation range: $210K - $250K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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