Enterprise AI Solutions Engineer

About the role

The Enterprise AI Solutions Engineer plays a critical role at Alvarez & Marsal, bridging the gap between technical execution and business impact. This role requires a deep understanding of AI technologies and a proven track record of delivering impactful solutions.

Responsibilities

• Drive the technical solution: Own end-to-end technical delivery across frontend, backend services, APIs, and cloud infrastructure.
• Partner closely with product managers and business line leads to identify where technology and AI can deliver the greatest impact.
• Communicate technical trade-offs clearly and credibly to non-technical stakeholders.
• Oversee implementation: Ensure delivery from design through deployment, manage and coordinate the work of internal developers and external development partners.
• Work with leadership to identify, design, and develop AI-powered solutions that improve organizational efficiency and reduce manual effort.
• Maintain clear and consistent communication across all levels of the organization, from engineering teams to executive leadership.
• Establish and uphold development practices, CI/CD pipelines, documentation standards, and code quality expectations.
• Proactively identify bottlenecks and unmet needs through close partnership with product and business stakeholders.

Qualifications

• 10+ years of full stack engineering experience, with at least 2 years in a tech lead or solution architect capacity.
• Strong background across frontend, backend services, APIs, and cloud infrastructure, with demonstrated ability to guide and evaluate the work of others.
• Designed and deployed data and AI solutions in Azure cloud.
• 2 – 5 years of experience with Generative / Agentic AI.
• Demonstrated background in solution architecture, including the design of distributed systems, scalable integrations, and multi-team delivery models.
• Hands-on experience building AI-powered applications, including LLM integrations, RAG architectures, vector databases, embeddings, and modern AI frameworks and SDKs.
• Demonstrated ability to engage directly with product and business stakeholders, from requirements gathering through implementation oversight.
• Exceptional communication and interpersonal skills, with the ability to engage credibly across engineering, product, and business leadership, adapting communication style to the audience.
• Demonstrated ability to operate effectively in ambiguous environments, establishing structure and clarity where requirements are not fully defined.
• Knowledge of enterprise security standards including SSO, SAML, OIDC, audit logging, and compliance controls is a plus.