Director, AI Data & Knowledge

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 Data & Knowledge Director owns the strategy, design, and operations of the data and knowledge layer underpinning all AI tools within the Global AI & Knowledge Organization. Reporting to the Chief AI & Knowledge Officer, this leader connects AI capabilities to firm-wide structured systems (EDW, Salesforce, Workday) and unstructured knowledge stores (SharePoint, engagement repositories) while advising Business Units on integration approaches tailored to their unique data and security contexts.

The ideal candidate is a strategic technology leader with sufficient technical depth to evaluate engineering decisions, direct architectural tradeoffs, and earn the trust of both technical teams and senior business. They bring strong product and systems thinking, a clear point of view on data governance, and the credibility to say this is the right approach and be believed.

This leader will play a critical role in modernizing data layers and operationalizing generative AI capabilities in production environments by leading a team of engineers and the firm's knowledge management tech stack.

How You Will Contribute

Data Layer Architecture

• Lead strategy, design, build and operations of the AI data layer across structured and unstructured sources, championing in-place access over unnecessary data movement
  
• Establish requirements for governed pipelines connecting enterprise systems (EDW, Salesforce, Workday, SharePoint, ERP) to AI consumption layers; make informed build/buy/partner decisions in partnership with the engineering lead
  
• Define the outcomes and standards for data quality, metadata management, and lineage tracking; hold the engineering team accountable to those standards
  

Unstructured Data & Knowledge Enablement

• Own the strategy for making firm knowledge AI-accessible - SharePoint, document libraries, engagement deliverables, and BU content stores - via federated indexing and retrieval rather than bulk extraction
  
• Own the retrieval backend for AI search - the index scope, permission inheritance model, and data quality requirements - in partnership with the Apps team who owns the user-facing search experience
  
• Define success criteria and requirements for how firm knowledge surfaces in AI tools, including chunking strategies, embedding pipelines and index refresh processes; partner with engineering on technical implementation of retrieval pipelines; partner with BU content owners on taxonomy and relevance requirements
  

Knowledge Graph & Knowledge Architecture

• Lead the knowledge management tech stack as part of this practice - at A&M, unlike many enterprise AI CoE structures, knowledge tech sits within the data layer, making knowledge architecture a first-class responsibility of this role
  
• Define the strategy for knowledge graph adoption - which firm knowledge should be modeled as relationships rather than retrieved as documents - and partner with engineering to design and implement
  
• Partner with subject matter experts, global KM and BU knowledge leads to develop taxonomies and metadata standards that make firm knowledge findable, reusable, and trustworthy at scale
  
• Define the strategy for connecting unstructured knowledge (engagement deliverables, practitioner expertise) to structured retrieval - enabling contextual AI responses grounded in A&M's institutional knowledge
  

Data Governance & Security

• Define requirements for a permission-aware data access model reflecting the firm's complex multi-BU structure; partner with Information Security and engineering to implement
  
• Define data classification standards, access tiers, and audit controls in collaboration with Information Security and enterprise data governance; navigate conflicting access requirements across BUs
  
• Ensure governance and security controls are embedded into data layer architecture by engineering teams, in support of the CoE's Responsible AI framework
  

Enterprise Integration & BU Advisory

• Serve as strategic owner for integrations with firm-wide systems; leading engineering team to develop reusable integration patterns and standards for the CoE tool portfolio
  
• Advise BUs on connecting proprietary datasets and SharePoint content to CoE AI tools - including data readiness, security constraints, and governance requirements - without requiring BUs to surrender data ownership
  
• Partner with the Apps, Marketplace, and BU leads to define how the data layer enables end-to-end AI use cases - translating the combined capabilities of each practice into a coherent picture of what's possible, then working with each team to define their specific contribution to making it work
  

Team Leadership

• Lead and grow a team of data engineers, software engineers, AI engineers, and knowledge tech professionals: goal-setting, performance management, and mentorship
  
• Partner with the CoE Tech Lead on engineering standards, delivery processes, staffing, and capacity planning
  
• Partner with the team's most senior engineer to evaluate technical architecture decisions, implementation approaches, and engineering tradeoffs
  

Qualifications

• 10+ years in AI product strategy, data strategy, or technical program leadership at enterprise scale; 3+ years leading cross-functional teams
  
• Demonstrated experience owning or directing RAG systems and AI search in production - sufficient technical fluency to evaluate architecture and make informed decisions without being the hands-on builder
  
• Demonstrated experience enabling AI access to unstructured content (SharePoint, document repositories) using in-place or federated retrieval - not wholesale data centralization
  
• Deep understanding of complex, multi-entity data governance and access control design; experience navigating conflicting security requirements across organizational boundaries
  
• Sufficient technical context to engage credibly with engineering teams and evaluate architectural tradeoffs; familiarity with Azure AI services (Azure AI Search, Azure AI Foundry) preferred
  
• Experience integrating enterprise systems (CRM, ERP, HCM, EDW) with AI or analytics platforms
  
• Experience with knowledge management, enterprise taxonomy, or knowledge graph strategy - or demonstrated ability to define requirements and lead in a domain with strong technical partners
  

Technical Fluency

RAG & Knowledge Retrieval

• Azure AI Search, hybrid/semantic search, federated retrieval, permission-aware indexing, Microsoft Graph API, SharePoint knowledge access

Data Engineering Concepts

• ETL/ELT patterns, enterprise system connectivity (CRM, ERP, HCM); familiarity with Azure data services

Knowledge Architecture

• Knowledge graphs, enterprise taxonomy, metadata standards, structured/unstructured knowledge integration strategy

Governance & Security

• Permission-aware retrieval, data classification, access control concepts (RBAC/ABAC), audit requirements, Azure Entra ID; familiarity with Azure AI Foundry

Preferred Qualifications

• Professional services or consulting environment experience
  
• Microsoft Copilot / Copilot Studio with custom connectors and SharePoint grounding
  
• MCP (Model Context Protocol) server patterns for AI-to-data-source integration
  
• Enterprise knowledge management practices in large, distributed organizations
  
• Bachelor's degree required; advanced degree in any field a plus
  

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 $225,000 - $275,000 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.

Must be authorized to work in the US without the need for employment-based sponsorship now or in the future. A&M will not sponsor applicants for US work visa status for this role.

#LI-LH1