Enterprise AI Architect

Alvarez & Marsal (A&M) is a global consulting firm with entrepreneurial, action and results-oriented professionals in over 40 countries. 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

We are a cross-disciplinary group bringing together enterprise architecture, data platforms, engineering, security, and delivery excellence to help clients design, govern, and scale AI solutions across complex environments. We partner with senior business and technology leaders to translate strategic outcomes into secure, scalable, and responsible AI capabilities that deliver measurable impact. Our work spans advisory and hands‑on delivery—defining target‑state architectures, establishing guardrails and governance, and guiding engineering teams from prototype to production.

How you will contribute

• Define enterprise AI architecture spanning data, cloud, applications, integration, and security across diverse technology estates.
• Design scalable AI solutions using modern AI, machine learning, and GenAI technologies, aligned to business outcomes and value cases.
• Translate business problems into practical AI use cases, solution designs, and delivery roadmaps with clear benefits, risks, and dependencies.
• Assess current-state platforms and identify data, platform, and integration changes needed to enable AI at scale.
• Establish architecture patterns, guardrails, and governance standards for responsible AI adoption, including security, privacy, and model risk controls.
• Partner with cloud, data, engineering, security, and business teams to progress AI use cases from prototype to production with robust MLOps/LMMOps practices.
• Lead technical decision-making across platforms, vendors, operating models, and delivery approaches, balancing speed, risk, and total cost of ownership.
• Advise senior stakeholders on technical trade‑offs, risks, interdependencies, and investment choices to inform portfolio and roadmap decisions.
• Drive measurable outcomes through automation, productivity improvements, cost reduction, and better use of enterprise data assets.

Qualifications

• Experience across enterprise architecture and solution architecture with exposure to cloud, data, or AI delivery in large organizations.
• Strong understanding of how AI solutions are designed, integrated, governed, and deployed at enterprise scale, including security and compliance considerations.
• Hands‑on familiarity with modern cloud and data platforms (e.g., AWS, Azure), and ecosystem tools (e.g., Snowflake, Databricks, or similar).
• Knowledge of GenAI, machine learning, orchestration and automation, data pipelines, APIs, integration patterns, and enterprise security controls.
• Ability to define pragmatic technical architectures without being limited to a single platform or vendor; experience evaluating build/buy/partner options.
• Proven track record moving AI use cases beyond pilots into resilient, observable, and cost-effective enterprise solutions.
• Comfortable operating from strategic target‑state design through delivery detail, including patterns, reference architectures, and implementation guidance.
• Excellent stakeholder management and communication skills, able to explain complex topics clearly to business and technology leaders.
• Experience in regulated, data‑heavy, or complex enterprise environments is advantageous.