AI Engineering Lead and Architect Intelligence Layer

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 desig