• Lead SLM candidate evaluation and selection: assess Small Language Model options for edge deployment against hardware constraints, inference latency requirements, domain restriction feasibility, and licensing. • Produce a technology assessment with explicit trade-off rationale and a recommended approach. • Design the domain restriction and guardrails architecture: define how the SLM is constrained to a known operational scope, how out-of-domain responses are prevented, and how the system enforces retrieval-first, non-authoritative behavior appropriate for a safety-adjacent environment. • Design the capability framework that structures how the system responds to operator queries — how capabilities are scoped and isolated, how the framework supports incremental addition of new interaction types over time, and what the prototype will implement. • Design the retrieval-augmented inference pipeline: define how the SLM retrieves context from a local knowledge store at inference time, including retrieval strategy, context injection approach, and latency budget appropriate for the edge environment. • Evaluate candidate cloud services for knowledge retrieval, model governance, and fleet-level model lifecycle management including over-the-air model distribution to edge devices. • Produce architecture recommendations aligned to client enterprise standards; all service selections are subject to client review and approval. • Define the offboard ML lifecycle: how models are evaluated, adapted through prompting and retrieval augmentation, versioned, governed, and distributed at scale. • Fine-tuning or custom model training is not a default commitment in this phase — adaptation approach will be determined based on discovery findings. • Collaborate with the Edge ML / Embedded Engineer on hardware constraint inputs that shape SLM selection and inference pipeline design, ensuring architecture recommendations are grounded in confirmed runtime feasibility. • Collaborate with the AWS Solutions Architect on candidate cloud service architecture for model governance, knowledge retrieval, and the model update pipeline, ensuring the cloud-side AI architecture aligns with the broader platform. • Document safety design principles and operational boundaries — authority separation, bounded AI behavior, explainability approach, and human-in-the-loop considerations — as architecture artifacts for client engineering and compliance review. • Produce all architecture recommendations as Architecture Decision Records (ADRs) with explicit trade-off rationale. • Clearly distinguish confirmed decisions from those that remain conditional on hardware specifications or interface access not yet confirmed.

• Partner with direct and indirect sales teams to acquire new Five9 customers and increase Five9 footprint within the install base • Drive and engage prospect facing solution discovery, with a focus on integrating Five9 technology into an organization’s existing infrastructure • Translate customer business needs/outcomes to technical solutions • Provide subject matter expertise to Five9 Sales Directors and Solution Consultants • Educate, enable, and empower Five9 direct and indirect sellers • Deliver customized solution demonstrations & presentations • Remain up to date on the latest technologies with respect to all things Contact Center and CX AI • Answer prospect/customer-initiated Requests for Proposal (RFP) and Requests for Information (RFI) • Effectively communicate opportunity engagement plans, progress, and status to both internal and customer organizations • Continually seek out opportunities to increase customer satisfaction and deepen client relationships by interacting effectively at all levels of the client organization • Develop materials in-line with specific sales and/or services opportunity requirements • Work with Professional Services to align on configuration of products for customers and ensure a seamless pre to post sales transition • Identify and initiate opportunities for continual team process improvement while working with cross team internal stakeholders • Work with Marketing and Product Managers to define new products & initiatives and provide insight from the field back to these groups • Must be able to travel nationally on short notice, up to 60% at times, providing on-site consulting work to clients in addition to working remotely from your home office

• Employ tools and techniques used to understand and analyze how to collect, update, store and exchange data • Define and employ data modeling and design standards, tools, best practices, and related development methodologies • Design, review and maintain data models • Perform data analysis activities to capture data requirements and represent them in data model visualizations • Manage the life cycle of the data model from requirements to design to implementation to maintenance • Work closely with data engineers to create optimal physical data models • Identify areas where data can be used to improve business activities

• Design and build production-ready AI/ML systems with high reliability and scalability • Work extensively with LLMs, RAG pipelines, and agentic frameworks • Fine-tune and optimize models for real-world performance and efficiency • Develop and deploy edge AI and computer vision solutions • Architect and implement distributed, high-availability AI systems • Optimize workloads across GPU architectures and compute environments • Build on-demand LLM systems and intelligent automation pipelines • Develop and research robotics teleoperation systems in simulation (MuJoCo, Isaac) • Build scene and task generation toolchains in robotics simulators • Develop reactive 3DGS-based end-to-end autonomous driving (E2E-AD) closed-loop simulators • Deploy and train E2E-AD algorithms (UniAD, VAD, VADv2, etc.)