• Acts as a liaison between end-users/clients and the MedImpact Business and Technology teams • Responsible for reviewing, analyzing, and evaluating the most complex business systems and user needs • Formulates system solutions to parallel overall business strategies • Writes detailed descriptions of user needs, program functions, and steps required to develop or modify computer programs • Interviews end-users/customers to define business requirements for complex software modifications and enhancements • Coordinates and responds to requests for changes from original specifications • Facilitates design sessions with business and technical resources • Offers recommendations and suggestions for process improvements • Defines and documents requirements and business rules • Drives and leads projects to completion and maintains accountability • Ensures that projects are completed on time, accurate to defined specifications and within budget

• Develop world-class GPU-accelerated AI inference serving software. • Contribute to feature development and drive broad customer adoption. • Drive the convergence of the Triton Inference Server and NVIDIA Dynamo stacks to establish a unified, high-performance inference platform. • Be an active member of the open source deep learning software engineering community. • Balance a variety of objectives such as building robust software designed to be deployed in production server or cloud environments, optimizing and balancing prediction throughput and latency, and developing and adopting the next generation of inference technologies.

• Serve as the principal technical advisor for assigned Mid-Telco and Service Provider accounts. • Build trusted relationships with customer executives, CTO organizations, network architects, engineering leaders, and operations teams. • Lead executive-level technology discussions focused on network modernization, automation, AI-driven operations, cloud connectivity, and next-generation routing architectures. • Conduct comprehensive discovery sessions to understand customer business priorities, technical requirements, operational challenges, and long-term network strategy. • Design scalable, resilient, and highly available networking solutions leveraging the HPE Networking portfolio with an emphasis on carrier-class routing, transport, and data center networking. • Architect and position solutions utilizing Juniper MX, PTX, and QFX, EX, SRX platforms supporting IP/MPLS core, edge, aggregation, peering, transport, and data center deployments. • Provide expert guidance on JUNOS and JUNOS EVO architectures, software lifecycle management, migration planning, and operational best practices. • Lead technical presentations, architectural workshops, demonstrations, proof-of-concepts, and customer design reviews. • Develop technical proposals, solution architectures, Bills of Materials, implementation strategies, and responses to RFPs and RFIs. • Collaborate with Product Management, Engineering, and the Office of the CTO to provide customer feedback that influences future product direction. • Partner across HPE organizations including AI, Compute, Storage, GreenLake, Security, and Services to deliver comprehensive business solutions. • Act as a technical leader throughout complex sales engagements, helping drive opportunities from qualification through successful closure. • Mentor Systems Engineers across the organization by sharing best practices, conducting technical enablement sessions, and serving as a senior escalation resource. • Stay current on emerging networking technologies, service provider trends, AI networking, automation, and cloud-native architectures.

• Audit the current product UI and existing design artifacts; identify the highest-leverage opportunities to standardize, consolidate, and reduce inconsistency. • Define the information architecture of the system itself — naming, taxonomy, component hierarchy, and documentation structure — so that both humans and AI agents can find, reason about, and correctly apply the right primitives. • Create a concrete step-by-step plan by partnering with design, engineering, and leadership, to correctly prioritize pieces of this effort, balancing long-term scalability with near-term product needs. • Define and ship a scalable token strategy (color, typography, spacing, elevation, motion) with a pipeline that keeps Figma variables, code tokens, and Storybook in sync. • Partner with Front-End engineers to translate the system into reusable, well-architected components with clear APIs, states, and accessibility built in. • Own the component lifecycle end-to-end: versioning, release notes, deprecations, and handoffs. • Build a lightweight, builder-friendly contribution process — designers, engineers, and PMs should be able to propose, review, and add to the system without a heavy governance tax. • Create documentation that people (and agents) actually use: guidelines, do/don't examples, usage rationale, and decision records, structured so they're machine-readable as well as human-readable. • Drive pragmatic adoption: start where it matters most, ship iteratively, and build credibility through visible product impact. • Define how AI agents help audit, tag, and maintain the system's structure — and where human review gates belong (naming decisions, deprecations, new pattern introductions). • Shape the pipeline that takes a Figma frame or a PM's prompt through Cursor / Claude Code into Storybook-validated components, including the review checkpoints that keep quality, accessibility, and brand integrity intact. • Make the system legible to agents. Ensure components, tokens, and patterns are documented, named, and structured so that when a designer or PM prompts an agent to "build the post-intent screen," the agent reaches for the right primitives and produces something shippable — not something that needs to be rebuilt. • Define what success looks like and track meaningful signals: adoption, UI consistency, design/dev cycle time, accessibility improvements, and the quality of AI-generated output against the system. • Partner with Research and Data where useful to connect system changes to product outcomes (quality, user confidence, conversion efficiency).