Job Summary
Leads solution architecture for complex, multi-domain initiatives for for AI, data, cloud, platforms and AI-enabled applications. Defines architectural standards, integration patterns, and non-functional requirements for developing scalable designs using hyperscaler and Tech OEM ecosystems and HCL products, ensuring security, performance, compliance, and Responsible AI. Ensures consistency, governance, and responsible technology adoption across solutions and teams.
Key Responsibilities
Lead architecture for complex, multi-domain initiatives and govern consistency across multiple teams and solutions.
Define architectural standards and reusable patterns for integration, scalability, security, compliance, and Responsible AI.
Drive design authority in cross-team forums; resolve trade-offs across platforms, data, AI lifecycle, and operations.
Partner with engineering leadership to ensure reference architectures are adopted and measurable outcomes are met.
Align solution roadmaps with ecosystem partners (hyperscalers/OEMs) and HCL product capabilities.
Mentor architects and contribute to architectural governance, documentation standards, and reusable blueprints.
Skill Requirements
1. Strategic Architecture & Scaled Solution DesignLead the end-to-end design and architecture of edge AI solutions, combining robotics, computer vision, and AI inference deployed on heterogeneous edge devices.Architect hybrid AI workflows spanning edge, near-edge, and cloud environments, ensuring low-latency, high-reliability inferenceBuild scalable AI and robotics workflows supporting real-time decision-making and autonomy in complex environments.Develop architecture for multi-site, distributed deployments with integration into operational technology (OT) systems like SCADA, MES, and healthcare devices.Drive hybrid edge-to-cloud AI deployments optimizing latency, throughput, and cost.2. Robotics & AI Model DevelopmentLead the development and deployment of robot policies, task planning, and intelligent automation using AI techniques like reinforcement learning, hierarchical planning, and sensor fusion.Oversee computer vision model development for perception tasks such as object detection, tracking, quality inspection, and anomaly detection optimized for edge inference.Implement robotics simulation and real-world testing pipelines using platforms like NVIDIA Isaac Sim, Gazebo, or MuJoCo to validate policies and control algorithms.Optimize AI and robotics models for target edge hardware with pruning, quantization, and GPU acceleration (TensorRT, CUDA).3. Platform Expertise & Integration· Utilize NVIDIA Jetson, IGX, EGX platforms and toolkits including NVIDIA Isaac, DeepStream, Metropolis, Holoscan, and the NGC software catalog for AI and robotics deployment.· Integrate hyperscaler edge AI offerings: AWS IoT Greengrass, Lambda@Edge, SageMaker Edge Manager; Azure IoT Edge, Percept, Azure Stack HCI; GCP Vertex AI, TensorFlow Lite for seamless edge-cloud orchestration.· Leverage OEM edge infrastructure from Dell NativeEdge and HPE Edgeline servers for ruggedized and scalable edge computing environments.o Edge Computing Architecture: Design patterns for edge networks, data aggregation, and processing. Familiarity with hardware accelerators (TPUs, GPUs, FPGAs) and embedded systems.o Containerization: Docker, Kubernetes for edge device orchestration.o Fog Computing: Knowledge of fog computing layers and the relationship with edge/cloud environments.o Data Processing Frameworks: Apache Kafka, Apache Flink, Apache Spark (for distributed data processing on the edge).4. Scaled Deployment & OperationalizationArchitect secure, robust AI/robotics model lifecycle management for fleet-wide OTA updates, monitoring, automated rollback, and anomaly detection.Design and enforce edge security, privacy, and compliance strategies suitable for industrial, healthcare, and transportation domains.Collaborate with cross-functional teams including embedded, hardware, software, and data science to deliver end-to-end operational AI robotic solutions.5. Innovation, IP Creation & Thought LeadershipDevelop reusable IP including reference architectures, accelerators, APIs, and demo kits for common robotics and edge AI industrial use cases.Mentor technical teams on cutting-edge edge AI, robotics frameworks, simulation, deployment best practices, and compliance.Evangelize edge AI and robotics capabilities internally and externally through presentations, workshops, and collaboration with technology partners.6. Client & Ecosystem EngagementAct as a trusted advisor to clients and stakeholders, translating business challenges into technically feasible edge AI and robotics solutions.Engage with NVIDIA, hyperscalers, OEMs, and ecosystem partners to stay current on emerging technologies and co-develop joint solutions.