• Be a key member of the Echodyne sales team responsible for both pre and post-sales technical engagement with Echodyne’s customer base. • Showcase Echodyne’s products by performing on-site radar demonstrations at external events, conferences, partner, and customer sites. • Support the formation of customer and partner relationships by presenting the value that our radar products bring to their solutions and applications. • Lead installation planning and integrations, execute deployment configuration and system tuning, and provide ongoing technical support and troubleshooting. • Manage relationships with existing and potential customers and partners while at demonstrations to ensure a positive impression. • Participate in post-demo data analysis and report creation. • Support the scaling of Echodyne’s customer and partner bases by developing and maintaining training materials, including application notes and user manuals. • Help improve Echodyne’s products by regularly collecting customer feedback and interfacing with Echodyne product managers and engineers. • Up to 50% travel may be required.

• Architect and oversee the deployment of scalable services using Amazon EKS and transition to AWS Lambda for high throughput microservices. • Map complex data flows between the portal and various internal/legacy data systems, ensuring reliable data synchronization and high availability. • Lead the design of the Node.js Backend-for-Frontend (BFF) for seamless frontend integration and high-performance microservices for core business logic. • Define API contracts (OpenAPI/Swagger), establish coding standards for Go and Node.js, and lead architectural reviews to prevent technical debt. • Design a robust "Front Door" strategy using AWS API Gateway to manage traffic, enforce security policies, and route requests across the BFF and backend service layers. • Partner with Product Owners to define roadmaps and provide technical mentorship to Principal Leads and Engineering teams. • Establish standards for CI/CD pipelines, automated scaling, and deep observability (tracing, logging, and metrics) across the distributed AWS environment.

• Lead technical discussions with key clients, understanding their requirements and challenges, and providing advanced technical solutions that align with their business objectives. • Drive solution development for key accounts, ensuring that solutions are tailored to meet specific customer needs and are technically robust. • Provide advanced technical guidance and expertise to both internal teams and clients, serving as a subject matter expert in the field. • Contribute to strategic sales initiatives by leveraging deep product knowledge and understanding of customer needs to support the development of effective sales strategies. • Collaborate cross-functionally with product management, engineering, and other teams to ensure alignment between customer requirements and product capabilities • Stay updated on industry trends, emerging technologies, and competitor offerings to provide insights and recommendations for product enhancement and innovation. • Act as a trusted advisor to clients, building strong relationships based on technical expertise, reliability, and a commitment to delivering value-added solutions. • Mentor and coach junior team members, sharing knowledge and best practices to help them develop their technical skills and advance in their careers. • Participate in product testing and feedback to provide meaningful, actionable feedback to improve product quality and user experience.

• Lead technical discovery and solution positioning for enterprise AI infrastructure opportunities, translating business outcomes into reference architectures and value propositions. • Own pre-sales deliverables including architectures, diagrams, sizing, BOMs, proposals. • Deliver executive and technical presentations focused on NVIDIA AI Enterprise (NVAIE), LLM training/inference, and accelerated analytics. • Guide clients through technology selection, roadmap development, and business case creation for large-scale AI initiatives. • Architect end-to-end AI platforms using NVIDIA DGX/HGX, Blackwell (B100/B200), Hopper (H100/H200), Grace/Grace-Hopper (GH200), L40S, NVLink/NVSwitch, InfiniBand (NVIDIA Quantum), RoCE, and DPU offload patterns. • Design solutions leveraging AMD Instinct (MI300/MI300X) as appropriate, articulating trade-offs in CPU/GPU/DPU, interconnect topology, and cluster scale-out. • Integrate NVIDIA AI Enterprise components (CUDA, cuDNN, TensorRT, Triton Inference Server, RAPIDS) and common ML frameworks (PyTorch, TensorFlow) with orchestration platforms. • Experience integrating on-prem GPU clusters with cloud AI services (AWS SageMaker, Azure ML, GCP Vertex AI) for hybrid bursting and workload mobility. • Advise on MLOps platforms (MLflow, Kubeflow, Weights & Biases), CI/CD, and governance for multi-tenant AI environments. • Build and maintain relationships with NVIDIA, AMD, Run:AI, OEMs, and networking vendors, aligning campaigns with partner programs and incentives. • Contribute feedback to vendor engineering and product teams, coordinating joint enablement and reference designs. • Create repeatable assets such as validated designs, sizing calculators, POV guides, deployment runbooks, and competitive playbooks. • Mentor SEs and delivery consultants, leading internal training on AI scheduling, performance tuning, and operational best practices. • Lead proof-of-value (POV) and proof-of-concept (POC) engagements, including success criteria, benchmarking, and recommendations.