AI Engineering Director

Description

AI Engineering Director

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 Engineering Lead owns the design, build, and operations of the data layer underpinning all AI tools within the Global AI & Knowledge Organization. Reporting to the AI & Data Chief Product 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 both a strategic technology leader and a strong individual contributor who can actively code, architect solutions, mentor engineering teams, and partner with business stakeholders to drive AI innovation across the enterprise.

This leader will play a critical role in modernizing data layers, and operationalizing machine learning and generative AI capabilities in production environments.

How you will contribute

Data Layer Architecture

* Lead design, build, and operations of the AI data layer across structured and unstructured sources, championing in-place access over unnecessary data movement

* Architect governed pipelines connecting enterprise systems (EDW, Salesforce, Workday, SharePoint, ERP) to AI consumption layers; apply ETL/ELT and streaming where data movement is genuinely warranted

* Enforce data modeling standards, metadata management, quality controls, and lineage tracking across the data layer

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

* Design and operate RAG systems and AI search capabilities (Azure AI Search, hybrid search, semantic ranking) that surface relevant content while inheriting source system permissions

* Develop chunking strategies, embedding pipelines, and index refresh processes; partner with Knowledge Management and BU content owners on taxonomy and relevance requirements

Data Governance & Security

* Design a permission-aware data access model reflecting the firm's complex multi-BU structure - ensuring AI tools surface only what the requesting user is authorized to see, inheriting ACLs from source systems

* Define data classification standards, access tiers, and audit controls in collaboration with Information Security and enterprise data governance; navigate conflicting access requirements across BUs

* Embed governance and security controls directly into data layer architecture in support of the CoE's Responsible AI framework

Enterprise Integration & BU Advisory

* Serve as technical owner for integrations with firm-wide systems; develop reusable connectors, API abstractions, and integration 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

Team Leadership

* Lead and grow a team of data engineers: goal-setting, performance management, mentorship, and upskilling in RAG systems and AI search

* Partner with the CoE Tech Lead on engineering standards, delivery processes, staffing, and capacity planning

Qualifications

* 10+ years in data engineering, including enterprise-scale platform design; 3+ years in a people leadership role

* Hands-on experience designing and operating RAG systems and AI search in production

* 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

* Strong proficiency in Python, SQL, and Azure data and AI services (ADF, ADLS, Azure AI Search, Microsoft Graph API)

* Experience integrating enterprise systems (CRM, ERP, HCM, EDW) with AI or analytics platforms

Core Technical Skills

RAG, AI Search & Unstructured Data

* Azure AI Search, hybrid/semantic search, federated retrieval, RAG architecture, embedding pipelines, Lang Chain / Llama Index, Microsoft Graph API, SharePoint indexing

Data Engineering & Integration

* Python, SQL, Spark, Databricks, Airflow, dbt, ETL/ELT, Kafka, Delta Lake, Salesforce / Workday / ERP…