AI Solution Engineer

Responsibilities

• As an AI Solution Engineer, you will lead the full cycle of AI-driven improvement — from surfacing opportunities to delivering working solutions. Your success is measured by business impact, stakeholder adoption, and the quality of change you drive.
• Efficiency Discovery & Process Assessment: Conduct structured discovery sessions with business units to map workflows, identify manual or repetitive processes, and surface opportunities where AI tools can meaningfully reduce time, cost, or error rates. Build and maintain a prioritized opportunity backlog with clear business cases and effort estimates.
• Stakeholder Engagement & Requirements Gathering: Build trusted relationships across functions — operations, finance, product, support, and others — to understand their day-to-day challenges and long-term goals. Facilitate workshops, interviews, and process walkthroughs to extract requirements and align on success criteria before any build begins.
• Solution Design & Technical Delivery: Translate business requirements into clear, scoped technical designs. Build and integrate AI-assisted solutions using existing platforms, APIs, and tools — working within established software systems rather than building ML models from scratch. Own the solution from design through deployment and handoff.
• Process Improvement & Change Management: Go beyond tooling — redesign the underlying processes that AI solutions will support. Identify upstream and downstream dependencies, document new workflows, and work with teams to ensure changes are adopted effectively. Track and report on efficiency gains post-implementation.
• Communication & Executive Reporting: Translate complex technical work into plain-language summaries, business cases, and executive presentations. Regularly communicate progress, tradeoffs, and outcomes to leadership. Be the person in the room who can bridge technical and non-technical perspectives without losing either audience.
• Internal AI Enablement: Help teams across the organization develop the confidence and capability to use AI tools effectively. Create guidance, playbooks, and lightweight training that reduce dependency on specialist support and build durable internal knowledge.
• Governance & Responsible Use: Ensure all solutions are deployed responsibly — with appropriate attention to data privacy, access controls, auditability, and alignment with company policy. Flag risks early and work with legal, security, and compliance stakeholders as needed.