• Lead commercial growth by defining and selling innovative systems engineering and managed services solutions, expanding client relationships. • Evolve and execute the global strategy for Systems Engineering & Support. • Lead enterprise transformation programs that modernize legacy systems, optimize operations, and harness AI and automation. • Build and evolve a high-performing global practice, mentoring leaders and teams across geographies. • Ensure delivery excellence for complex systems engineering and managed services initiatives, overseeing solution design, integration, and end-to-end operations.

• Lead commercial growth by defining and selling innovative systems engineering and managed services solutions • Evolve and execute the global strategy for Systems Engineering & Support • Lead enterprise transformation programs that modernize legacy systems, optimize operations, and harness AI and automation • Build and evolve a high-performing global practice, mentoring leaders and teams • Ensure delivery excellence for complex systems engineering and managed services initiatives • Drive adoption of AI, automation, and emerging technologies across the practice • Monitor industry trends and client needs to continuously evolve practice offerings and delivery models • Serve as a trusted advisor to executive clients

• Provide pre-sales technical support and technical expertise to our customers and partners with objective to achieve sales targets. • Establish trusted advisory role with strategic customers and partners (CIO, CISO). • Enable and/or update partner network on product evolution. • Perform workshops with prospects or customers on product evaluations and do demonstrations. • Perform hands-on tests to validate customer proof-of-concept setups, network designs, and network deployments using new products and feature. • Respond to RFI’s/RFP’s. • Provide market and product feedback to product management and back-office. • Keep up-to-date on competitive solutions, products, and services. • Author whitepapers, knowledge base articles, and other customer/partner facing content.

• Design end-to-end model lifecycle patterns (MLOps) to boost velocity of development for ML engineers, including data preparation, model management, experiment tracking, and more • Zero-to-one development and support of a graph ML codebase and platform that abstracts away common patterns and enables greater model scalability and iteration • Collaborate with ML engineers on performance tuning, including improving model training time, efficiency, and GPU training costs in a large, distributed ML training environment • Optimize batch data processing within a data warehouse and with tools such as Apache Beam, Apache Spark, Ray Data, and more • Architect pipelines to build and maintain massive graph data structures on the order of billions of nodes and tens of billions of edges