The Senior AI Engineer will support the development of Alvarez and Marsal’s Intelligence Layer, a knowledge graph platform designed to enable secure global and business-unit-specific search and AI experiences. This hands-on role spans architecture support, knowledge graph design, ingestion, retrieval, APIs, and production-ready agentic pipelines with LLM and MCP integrations.

Role Overview

The Intelligence Layer is a knowledge graph-based platform that makes the firm’s structured and unstructured knowledge available through global and business unit specific search experiences. The Senior AI Engineer will work closely with the AI Engineering Lead and Architect, the Product Owner, internal engineering teams, and delivery partners to establish internal ownership and contribute directly to core platform capabilities.

Key Responsibilities

Build and maintain Intelligence Layer components, including ingestion pipelines, knowledge graph models, retrieval services, APIs, and user-facing capabilities.
Develop and optimize graph schemas and queries using Neo4j and Cypher or comparable graph technologies.
Contribute across the stack, with strength in back-end engineering and Python, and the ability to support modern front-end development when needed.
Integrate structured data and unstructured content from enterprise systems while preserving source permissions, metadata, and governance requirements.
Create integrations with large language model platforms and apply the Model Context Protocol (MCP) so Intelligence Layer capabilities are available to approved AI tools.
Build, test, and operate production agentic pipelines, including multi-step orchestration, tool routing, MCP-based tool use, retrieval, state management, evaluation, guardrails, observability, and human-in-the-loop controls.
Partner with the Lead and Architect and delivery partners on implementation, testing, debugging, code review, and technical documentation.
Develop secure, maintainable software aligned with requirements for client confidentiality, access control, reliability, and observability.
Surface risks and blockers early and help the team make practical technical decisions as priorities evolve.
Support delivery of the initial production iteration of the Intelligence Layer knowledge graphs.
Deliver reusable agentic pipeline components to support secure, governed workflows across business units and use cases.
Enable global search and business unit specific search across approved structured and unstructured data sources.
Deliver tested ingestion, graph, retrieval, API, and user-interface components that internal teams can operate and extend.
Establish reusable engineering patterns and documentation to support additional business units and data sources.

Required Qualifications

Five or more years of software engineering experience building production applications or data platforms.
Hands-on experience with knowledge graphs, graph data modeling, and graph query languages, ideally Neo4j and Cypher.
Full-stack development experience with strong Python and back-end skills, plus working experience with a modern front-end framework.
Experience designing, building, and consuming APIs and integrating multiple structured and unstructured data sources.
Practical experience with Claude, ChatGPT, or comparable large language model platforms.
Hands-on experience building production agentic pipelines or workflows, including orchestration, tool use, retrieval, state management, evaluation, guardrails, observability, or human oversight.
Working knowledge of the Model Context Protocol, including how MCP servers expose tools and data to AI clients.
Experience working directly with third-party delivery vendors on implementation, technical review, issue resolution, and delivery coordination.
Strong problem-solving, communication, and documentation skills.
Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Preferred Qualifications

Hands-on experience building MCP servers, agent tools, or custom AI integrations.
Experience with Microsoft Azure services for application hosting, data, search, identity, or AI.
Experience implementing enterprise authentication and authorization using Microsoft Entra ID, OAuth 2.0, OpenID Connect, token scoping, or on-behalf-of flows.

Technologies

Neo4j, Cypher
Python
Claude, ChatGPT
Model Context Protocol (MCP), MCP servers
Large language model platforms
Microsoft Azure
Microsoft Entra ID
OAuth 2.0, OpenID Connect
Token scoping, on-behalf-of flows

First Year Outcomes

Help deliver the initial production iteration of the Intelligence Layer knowledge graphs.
Help deliver reusable agentic pipeline components supporting secure, governed workflows across business units and use cases.
Enable global search and business unit specific search across approved structured and unstructured data sources.
Deliver tested ingestion, graph, retrieval, API, and user-interface components that internal teams can operate and extend.
Establish reusable engineering patterns and documentation to support additional business units and data sources.

Location

New York, NY (onsite)

Compensation

USD 190,000 - 210,000 per year.

The salary range is dependent on several variables, including education, experience, skills, and geography. Alvarez and Marsal also offers a discretionary bonus program based on factors including individual and firm performance. Please ask your recruiter for details.

Benefits

Healthcare plans
Flexible spending and savings accounts
Life, AD&D, and disability coverages
401(k) retirement savings plan
Annual discretionary contribution to their 401(k) retirement savings plan
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
Click here for more information regarding A&M’s benefits programs.

How You Will Contribute

Build and maintain components of the Intelligence Layer, including ingestion pipelines, knowledge graph models, retrieval services, APIs, and user-facing capabilities.
Develop and optimize graph schemas and queries using Neo4j and Cypher or comparable graph technologies.
Contribute across the stack with a focus on back-end engineering and Python, and support front-end development when needed.
Integrate structured data and unstructured content from enterprise systems while preserving permissions, metadata, and governance requirements.
Build integrations with large language model platforms and apply MCP to make Intelligence Layer capabilities available to approved AI tools.
Build, test, and operate production agentic pipelines, including multi-step orchestration, tool routing, MCP-based tool use, retrieval, state management, evaluation, guardrails, observability, and human-in-the-loop controls.
Collaborate with the Lead and Architect and delivery partners on implementation, testing, debugging, code review, and technical documentation.
Develop secure, maintainable software aligned with client confidentiality, access control, reliability, and observability requirements.
Communicate risks and blockers early and support practical technical decisions as priorities evolve.

Senior Manager, AI Engineer Intelligence Layer

Job Description