Description

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.

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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