AI - Enablement Engineer
AI Solution Design & Implementation Act as the technical owner and developer of AI-powered automation projects—from discovery through delivery—across internal business domains (e.g. finance, operations, HR, customer support, product development). Translate ambiguous business problems into concrete AI/ML opportunities and deliver working solutions using tools like large language models, intelligent agents, workflow automation, and custom software integrations. Build prototypes and minimum viable solutions (MVS), demonstrate ROI, and work with engineering or ops teams to scale or productionize. Engage directly with business stakeholders, SMEs, and process owners to identify high-impact opportunities for AI enablement. Collaborate with global engineering, data, and automation teams to deliver and support deployed solutions—often working with offshore counterparts. Serve as a technical liaison across functions, ensuring solutions meet business needs, compliance standards, and technical best practices. Establish a scalable model for rolling out AI automation solutions across business centers: build → enable → transition → replicate. Builds Extraordinary Teams: Drives impact through collaboration and influence by fostering trust, sharing expertise, and aligning efforts across teams. Delivers Results: Leads complex initiatives to successful completion with high standards, precision, and urgency. Create enablement artifacts (templates, documentation, reusable code, training resources) to allow teams to self-sustain and expand AI usage after the initial implementation. Provide mentorship and onboarding support to internal teams inheriting automation frameworks or tools. Design playbooks and re-usable modules that accelerate repeat adoption across other business units. Bachelor's or master's degree in Computer Science, Data Science, or related field. Ability to take ownership of the entire AI solution lifecycle—from problem framing to deployment to handoff. Ability to travel 25-30% is required. Experience designing intelligent automation in business process areas (e.g. document processing, ticket triage, reporting workflows). Knowledge of prompt engineering, agent frameworks, or LLM orchestration patterns is a strong plus. Familiarity with MLOps or model lifecycle management tools is helpful but not required. Experience working in organizations with federated operating models or shared services.
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AI - Enablement Engineer
AI Solution Design & Implementation Act as the technical owner and developer of AI-powered automation projects—from discovery through delivery—across internal business domains (e.g. finance, operations, HR, customer support, product development). Translate ambiguous business problems into concrete AI/ML opportunities and deliver working solutions using tools like large language models, intelligent agents, workflow automation, and custom software integrations. Build prototypes and minimum viable solutions (MVS), demonstrate ROI, and work with engineering or ops teams to scale or productionize. Engage directly with business stakeholders, SMEs, and process owners to identify high-impact opportunities for AI enablement. Collaborate with global engineering, data, and automation teams to deliver and support deployed solutions—often working with offshore counterparts. Serve as a technical liaison across functions, ensuring solutions meet business needs, compliance standards, and technical best practices. Establish a scalable model for rolling out AI automation solutions across business centers: build → enable → transition → replicate. Builds Extraordinary Teams: Drives impact through collaboration and influence by fostering trust, sharing expertise, and aligning efforts across teams. Delivers Results: Leads complex initiatives to successful completion with high standards, precision, and urgency. Create enablement artifacts (templates, documentation, reusable code, training resources) to allow teams to self-sustain and expand AI usage after the initial implementation. Provide mentorship and onboarding support to internal teams inheriting automation frameworks or tools. Design playbooks and re-usable modules that accelerate repeat adoption across other business units. Bachelor's or master's degree in Computer Science, Data Science, or related field. Ability to take ownership of the entire AI solution lifecycle—from problem framing to deployment to handoff. Ability to travel 25-30% is required. Experience designing intelligent automation in business process areas (e.g. document processing, ticket triage, reporting workflows). Knowledge of prompt engineering, agent frameworks, or LLM orchestration patterns is a strong plus. Familiarity with MLOps or model lifecycle management tools is helpful but not required. Experience working in organizations with federated operating models or shared services.