Collaborate with solution architects, AI engineers, and software engineering teams to translate business requirements into secure, scalable, production-ready solutions.
Gather business requirements through various methods such as interviews, document analysis, workshops, and workflow analysis, articulating these requirements in terms of target user roles and objectives. Translate business and user needs into clear technical requirements, solution designs and measurable success criteria for AI applications.
Change Management: Managing changes to project scope, timelines, or resources, assessing the impact of changes, and implementing change control processes to maintain project integrity and minimize disruptions. Adapt delivery choices as technologies and business priorities evolve, while supporting the adoption of new AI-enabled ways of working.
Knowledge Base Development: Design and implement structured knowledge bases that enhance LLM output quality by integrating domain-specific data, ontologies, and automated document indexing.
Kubernetes Service PlatformDevelopment of managed Kubernetes services with GPU-native orchestration, intelligent scheduling, distributed workload management, cluster lifecycle management, observability, resource isolation, and support for large-scale AI training and inference workloads
Have the discipline to provide timely and accurate prospect information and sales activity data for the purposes of management reporting and sales forecasting is a must;
Focus on building algorithms for the extraction, transformation and loading of large volumes of real-time, unstructured data (marketing assets, brand collateral, campaign performance data, client media libraries) to deploy AI or ML solutions from theoretical data science models into production services.
Assist in monitoring project performance through reporting, dashboards, KPI tracking, risk/issue management, and contribute to process standardization and continuous improvement initiatives.