• Design, build, and maintain scalable training infrastructure for computer vision workloads • Implement and manage distributed training pipelines (multi-GPU, multi-node) to support large-scale model training and hyperparameter tuning • Build and maintain robust data pipelines for ML development • Design database schemas and storage strategies for managing large training datasets, annotations, and model artifacts • Implement and manage feature stores, data versioning, and experiment tracking to support reliable model iteration • Automate existing analysis workflows • Maintain clear documentation for platform components, data contracts, and deployment processes • Communicate infrastructure decisions, tradeoffs, and system limitations clearly to ML engineers and stakeholders • Conduct thorough code reviews and write integration tests for ML pipelines

Role Description We are seeking a highly skilled AI Infrastructure & Kubernetes Platform Engineer with a proven track record in deploying and managing NVIDIA DGX-based AI clusters, orchestrating containerized AI workloads using Kubernetes, and ensuring secure, high-throughput operations across InfiniBand-powered networks. The ideal candidate will hold a combination of Kubernetes certifications (CKA, CKAD, CKS) and NVIDIA certifications (NCA-AIIO, NCP-AIO, NCP-AII, NCP-AIN), coupled with hands-on training in DGX, BlueField, and high-speed network operations. This position plays a key role in supporting AI/ML infrastructure at scale, enabling efficient training and inference for complex models, and integrating NVIDIA's cutting-edge compute, storage, and fabric solutions with modern DevOps practices. - AI Infrastructure Operations - Deploy and manage NVIDIA DGX BasePODs and SuperPODs for high-performance AI workloads. - Oversee DGX system lifecycle operations including provisioning, monitoring, firmware upgrades, and capacity planning. - Operate Base Command Manager to manage GPU clusters, schedule workloads, and integrate with MLOps tools. - Perform DGX node health validation, NCCL interconnect testing, and NVLink topology verification following new deployments or hardware changes. - Kubernetes Platform Engineering - Architect secure and scalable Kubernetes clusters optimized for GPU-accelerated workloads using NVIDIA GPU Operator. - Leverage expertise from CKA/CKAD/CKS to develop, deploy, and secure AI applications on Kubernetes. - Implement CI/CD pipelines and GitOps methodologies for deploying and managing ML workflows. - High-Performance Networking & DPUs - Administer InfiniBand networks and BlueField DPUs using Unified Fabric Manager (UFM). - Enable NVLink/NVSwitch performance across GPU nodes and tune fabric configurations for minimal latency and maximum throughput. - Use BlueField for offloading storage, firewalling, and telemetry, enhancing AI workload security and performance. - Security & Compliance - Apply best practices from the CKS certification to secure containerized AI environments. - Configure runtime security, secrets management, network segmentation, and auditing using DPU-enhanced Kubernetes deployments. - Support zero-trust architecture initiatives by enforcing workload identity, RBAC policies, and supply chain integrity across AI container images and model artifacts. - Monitor GPU, CPU, and I/O performance using NVIDIA DCGM, Prometheus, Grafana, and Base Command APIs. - Tune system performance and model training pipelines for cost-efficiency and throughput. - Build and maintain operational runbooks, incident response playbooks, and SLA reporting dashboards covering GPU utilization, thermal thresholds, and fabric health. Qualifications - Kubernetes certifications (CKA, CKAD, CKS) - NVIDIA certifications (NCA-AIIO, NCP-AIO, NCP-AII, NCP-AIN) - Hands-on training in DGX, BlueField, and high-speed network operations Requirements - Expertise with DGX System, BasePOD, and SuperPOD Administration - BlueField DPU Configuration & Operations - InfiniBand Fabric and UFM Management - Base Command Manager for workload orchestration Benefits - Remote position

• TrueTandem is seeking a Power Platform Developer to create and maintain robust, responsive, and scalable applications within a Federal environment. • This role involves hands-on Power Platform development of canvas apps that streamline and automate organizational business processes. • The candidate will develop application Proof-of-Concepts (PoCs) and Minimal Viable Products (MVPs) to demonstrate and iteratively build on their business value and functionality. • The developer should be able to create Canvas Apps from scratch or using data from an existing data source. • This includes understanding the user interface, controls, and screen navigation for forms and views. • This position requires experience and passion for coding, and a strong desire to solve our customers’ unique technology challenges.

• Define the technical vision and multi-year roadmap for the platform, including how we evolve from today's fleet to the next order-of-magnitude in scale, sensor count, and autonomy maturity. • Own the future architecture of our Ubuntu/Yocto/Linux distributions tailored for real-time, safety-critical autonomous vehicle workloads, and make the build-vs-buy decisions that follow. • Set the strategy for ROS 2 IPC middleware (Cyclone DDS, Fast DDS, Zenoh, etc.) across the fleet — including profile selection, QoS standards, and determinism budgets for multi-sensor data flows. • Lead development of user-space drivers for LiDARs, cameras, radars, GNSS/INS, CAN, and other vehicle interfaces, and set the standards other engineers follow when adding new hardware. • Own the platform's functional safety and security strategy end-to-end — secure boot, OTA update pipelines, CVE response, and alignment with ISO 26262 / SOTIF workflows as we mature toward production. • Define the observability contract for the platform: what "healthy" looks like in the lab and in the field, and the SLIs/SLOs the autonomy and perception teams can build against. • Collaborating with autonomy, perception, and controls leads to set cross-stack performance budgets (CPU, GPU, memory, bus bandwidth, end-to-end latency) and drive the cross-team work to hit them. • Set standards for how the platform is built, tested, and released — CI/CD for OS images and driver packages, hardware-in-the-loop testing, release gates, and rollback strategy. • Contribute to the platform team's technical hiring and calibration — own the interview rubric, grow senior engineers into tech leads, and raise the bar on code review and design review across the team. • Represent AeroVect technically in relationships with silicon, sensor, and middleware vendors, and influence their roadmaps where it matters to us. • Provide on-call escalation support for platform components during field trials and customer pilots, and use what you learn in the field to drive systemic fixes. • Identify strategic technical debt and drive it down — not just within the platform, but stack-wide.