Enterprise AI Solutions Engineer

About the role

The Enterprise AI Solutions Engineer at Alvarez & Marsal sits at the intersection of engineering, product, and practical AI delivery. This role bridges technical execution and business impact, serving as a senior technical leader responsible for designing, building, and implementing intelligent technology solutions.

Responsibilities

• Drive the Technical Solution: Own end-to-end technical delivery across frontend, backend services, APIs, and cloud infrastructure. Ensure quality, reliability, and production readiness.
• Product and Business Collaboration: Partner closely with product managers and business line leads to identify where technology and AI can deliver the greatest impact. Contribute to requirements definition, challenge assumptions, and provide a grounded engineering perspective in strategic planning discussions.
• Oversee Implementation: Manage and coordinate the work of internal developers and external development partners, ensuring alignment on requirements, standards, and delivery expectations. Define and enforce engineering practices including code review, testing, and release gating.
• AI Use Case Development: Work with leadership to identify, design, and develop AI-powered solutions that improve organizational efficiency and reduce manual effort. Collaborate with business and product partners to assess where AI can deliver the most meaningful value, then lead the effort to bring those opportunities to production as reliable, scalable tools and workflows.
• Stakeholder Communication: Maintain clear and consistent communication across all levels of the organization, from engineering teams to executive leadership. Frame problems with precision, articulate technical decisions in accessible language, and ensure all parties remain informed and aligned throughout the project lifecycle. Build trust with internal clients through consistent delivery and proactive communication.
• Engineering Standards and Mentorship: Establish and uphold development practices, CI/CD pipelines, documentation standards, and code quality expectations. Lead and mentor offshore teams to create architecture and development.

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.
• Proven experience leading and coordinating development teams, including internal engineers and external vendors or contractors.
• 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.